› sites › default › files › resources › GMLC Sensing Measurement Strategy... Sensing and...

NOTICE: This document contains information of a preliminary nature and is not intended for release. It is subject to revision or correction and therefore does not represent a final report.

Sensing and Measurement Technology Roadmap

Devices Including Communications and Data Analytics Requirements

February 2019

D. Tom Rizy, PI Paul Ohodnicki, PlusOne GMLC Sensing & Measurement Strategy Project Team

ii

DOCUMENT AVAILABILITY

Reports produced after January 1, 1996, are generally available free via US Department of Energy (DOE) SciTech Connect. Website www.osti.gov Reports produced before January 1, 1996, may be purchased by members of the public from the following source: National Technical Information Service 5285 Port Royal Road Springfield, VA 22161 Telephone 703-605-6000 (1-800-553-6847) TDD 703-487-4639 Fax 703-605-6900 E-mail [email protected] Website http://classic.ntis.gov/ Reports are available to DOE employees, DOE contractors, Energy Technology Data Exchange representatives, and International Nuclear Information System representatives from the following source: Office of Scientific and Technical Information PO Box 62 Oak Ridge, TN 37831 Telephone 865-576-8401 Fax 865-576-5728 E-mail [email protected] Website http://www.osti.gov/contact.html

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

“This research was supported by the Grid Modernization Initiative of the U.S. Department of Energy as part of its Grid Modernization Laboratory Consortium, a strategic partnership between DOE and the national laboratories to bring together leading experts, technologies, and resources to collaborate on the goal of modernizing the nation’s grid.”

http://www.osti.gov/

http://classic.ntis.gov/

http://www.osti.gov/contact.html

ORNL/SPR-2018/963

Sustainable Electricity Program

SENSING AND MEASUREMENT TECHNOLOGY ROADMAP

INCLUDING COMMUNICATIONS AND DATA ANALYTICS REQUIREMENTS

GMLC PROJECT 1.2.5 SENSING AND MEASUREMENT STRATEGY PROJECT

Author(s)

D. Tom Rizy, ORNL, PI

Paul Ohodnicki, NETL, PlusOne and Task Coordinator

Project Team Key Contributors: Zhi Li (ORNL), Emma Stewart (LLNL), Sydni Credle (NETL), Yarom

Polsky (ORNL), Paul Ohodnicki (NETL), Olga Lavrova (SNL), Venkat Krishnan (NREL), Guodong Liu

(ORNL), Peter Fuhr (ORNL), Chen (ANL), Emma Stewart (LLNL), Philip Top (LLNL), Matthew Lave

(SNL), and Steven Bossart (NETL).

Date Published: February 2019

Prepared by

OAK RIDGE NATIONAL LABORATORY

Oak Ridge, TN 37831-6283

managed by

UT-BATTELLE, LLC

for the

US DEPARTMENT OF ENERGY

under contract DE-AC05-00OR22725

iii

CONTENTS

PREFACE .................................................................................................................................................... v

ABBREVIATIONS, ACRONYMS, AND INITIALISMS .................................................................... vii

ACKNOWLEDGEMENTS ...................................................................................................................... ix

EXECUTIVE SUMMARY OF THE KEY RECOMMENDATIONS ........................................... 1 1.1 CROSSCUTTING SENSING AND MEASUREMENT SUPPORT .................................... 3 1.2 USES AND SENSING TECHNOLOGY TARGETS ............................................................ 1 1.3 COMMUNICATION AND NETWORKS .............................................................................. 2 1.4 DATA MANAGEMENT AND ANALYTICS INCLUDING GRID MODELING ............. 3

TECHNOLOGY ROADMAP ORGANIZATION .......................................................................... 5

BACKGROUND AND CONTEXT ................................................................................................... 6

SENSING AND MEASUREMENT IN THE GMI .......................................................................... 8

GMLC SENSING AND MEASUREMENT STRATEGY PROJECT ........................................ 12

SENSOR AND MEASUREMENT TECHNOLOGY ROADMAP PROCESS ........................... 16

TECHNOLOGY REVIEW AND ASSESSMENT DOCUMENT FINDINGS ............................ 22

WORKING GROUP GAP ANALYSIS RESULTS SUMMARY ................................................ 25 8.1 USES AND SENSING TECHNOLOGY TARGETS .......................................................... 25

8.1.1 Issues to Monitor .......................................................................................................... 25 8.1.2 Advanced Materials and Techniques ......................................................................... 26 8.1.3 Needed Advancements ................................................................................................. 26

8.2 COMMUNICATION AND NETWORKS ............................................................................ 30 8.2.1 Utilization and Integration .......................................................................................... 30 8.2.2 Architecture .................................................................................................................. 31 8.2.3 Standards and Protocols .............................................................................................. 31

8.3 Data Management and Analytics Including Grid Modeling ............................................... 33 8.3.1 Data Management ........................................................................................................ 33 8.3.2 Data Analytics .............................................................................................................. 34

CROSSCUTTING ISSUES .............................................................................................................. 38 9.1 CYBER-PHYSICAL ............................................................................................................... 38 9.2 STANDARDS, TESTING, AND STANDARDIZATION ................................................... 38 9.3 VALUE PROPOSITION ........................................................................................................ 38 9.4 FACILITATING DEPLOYMENT OF NEW TECHNOLOGIES ..................................... 39

CROSSCUTTING SENSING AND MEASUREMENT SUPPORT ............................................ 43 10.1 CYBER-PHYSICAL SECURITY AWARENESS AND SUPPORT .................................. 43 10.2 STANDARDS AND TESTING TO SUPPORT IMPROVEMENT OF SENSOR

PERFORMANCE, RELIABILITY, RESILIENCY, AND INTEROPERABILITY ....... 44 10.3 VALUATION OF SENSING AND MEASUREMENT TECHNOLOGY ......................... 45 10.4 GENERAL CROSSCUTTING NEEDS SUPPORT FOR INDUSTRY AND

UTILITY PARTNERS IN TECHNOLOGY DEPLOYMENT .......................................... 46

HIGH-VALUE USE CASES AND THE EXTENDED GRID STATE DEFINITION ............... 48 11.1 FAULT DETECTION, INTERRUPTION AND SYSTEM RESTORATION .................. 48 11.2 INCIPIENT FAILURE DETECTION IN ELECTRICAL GRID ASSETS...................... 49

iv

11.3 SENSING AND MEASUREMENT TECHNOLOGY TO MITIGATE AGAINST

IMPACTS OF NATURAL DISASTERS AND ENHANCE GRID RESILIENCE .......... 52 11.4 SUMMARY OF USE CASES ................................................................................................ 52

KEY FINDINGS AND PROPOSED FEDERAL EFFORTS TO ADDRESS GAPS.................. 54 12.1 USES AND SENSING TECHNOLOGY TARGETS .......................................................... 54 12.2 COMMUNICATION AND NETWORKS ............................................................................ 55 12.3 DATA MANAGEMENT AND ANALYTICS INCLUDING GRID MODELING ........... 56

PROPOSED RESEARCH THRUSTS INCLUDING METRICS ................................................ 58

APPENDIX A. DEFINITIONS .............................................................................................................. A-1

APPENDIX B. CYBER-PHYSICAL SECURITY ............................................................................... B-1

APPENDIX C. SENSOR AND MEASUREMENT TECHNOLOGY ROADMAP PROCESS ...... C-1

APPENDIX D. WORKING GROUP REPORT SUMMARIES ......................................................... D-1

APPENDIX E. USE CASES ................................................................................................................... E-1

v

PREFACE

This report was prepared by the Grid Modernization Lab Consortium Sensing and Strategy Project Team

for task 2 (sensor technology R&D roadmap development).

Principal Investigator:

D. Tom Rizy, Oak Ridge National Laboratory (ORNL)

Email: [email protected]

Phone: (865) 574-5203

PlusOne and Task 2 Lead:

Paul Ohodnicki, National Energy Technology Laboratory (NETL)


Phone: (412) 386-7389

GMLC Sensor Area Lead:

Tom King, Oak Ridge National Laboratory


Phone: (865) 241-5756

DOE Laboratory Working Group Leads:

Crosscutting Sensing and Measurement Support: Zhi Li (ORNL)

Use Case Refinement and Extended Grid State Integration: Emma Stewart (LLNL)

Harsh Environment Sensors for Flexible Generation: Sydni Credle (NETL)

Phasor Measurement Units for Grid State and Power Flow: Yarom Polsky (ORNL)

Asset Health Monitoring: Paul Ohodnicki (NETL)

Novel Transducers: Olga Lavrova (SNL)

Sensors for Weather Monitoring and Forecasting: Venkat Krishnan (NREL)

End-Use/Buildings Monitoring: Guodong Liu (ORNL)

Distributed Architectures: Peter Fuhr (ORNL)

Communications Technology: Chen Chen (ANL)

Advanced Analytics: Emma Stewart (LLNL)

Big Data: Philip Top (LLNL)

Other members and participants of the project are identified in the appendix.

mailto:[email protected]



vii

ABBREVIATIONS, ACRONYMS, AND INITIALISMS

AGC automatic generation controller

ANL Argonne National Laboratory

CA California

CIP Critical Infrastructure Protection (NERC standard)

DER distributed energy resource

DGA dissolved gas analysis

DHI diffuse horizontal irradiance

DNI direct normal irradiance

DOE US Department of Energy

EGS extended grid state

EERE Office of Energy Efficiency and Renewable Energy

EPRI Electrical Power Research Institute

ES energy storage

FDD fault detection and diagnosis

GHz gigahertz

GHI global horizontal irradiance

GMLC Grid Modernization Lab Consortium

GMI Grid Modernization Initiative

GPS geographic positioning system

ICS incident command system

IEEE Institute of Electrical and Electronics Engineers

IIoT Industrial Internet of Things

INL Idaho National Laboratory

IoT Internet of Things

ISO independent system operator

IT information technology

ITC information technology and communications

kWh kilowatt hour

LANL Los Alamos National Laboratory

LBNL Lawrence Berkeley National Laboratory

LED light-emitting diode

LLNL Lawrence Livermore National Laboratory

MHz megahertz

MWh megawatt hour

MYPP Multi-Year Program Plan

NASPI North American Synchrophasor Initiative

NERC North American Electricity Reliability Corporation

NETL National Energy Technology Laboratory

NFV network function virtualization

NREL National Renewable Energy Laboratory

viii

ORNL Oak Ridge National Laboratory

O&M operations and maintenance

OE Office of Electricity

OT operational technology

PMU phasor measurement unit

PNNL Pacific Northwest National Laboratory

POA plane of array

p.u. per unit

PV photovoltaic

PWST passive wireless sensor technology

QoS quality of service

R&D research and development

RMS root-mean squared

ROCOF rate of change of frequency

SCADA supervisory control and data acquisition

SDN software-defined networking

SNL Sandia National Laboratories

SPOT sensor placement optimization tool

T&D transmission and distribution

THD total harmonic distortion

UAV unmanned aerial vehicle

ix

ACKNOWLEDGEMENTS

This report was sponsored by the US Department of Energy (DOE) Office of Electricity (OE) and the

Office of Energy Efficiency and Renewable Energy (EERE). The project was directed and supported by

DOE Program Managers Kerry Cheung of OE and Marina Sofos of EERE.

The Grid Modernization Lab Consortium (GMLC) Sensing and Measurement Strategy Project, which

involved multiple national laboratory team members, was led by Oak Ridge National Laboratory. The

task 2 effort, which involved the development of (1) a technical review document on the state of the art in

sensors1 and (2) a document (this report) on sensor technology research and development needs was led

by the task lead at the National Technology Energy Laboratory.

Working groups consisting of national laboratory personnel and industry members were formed to

expedite the development of this document. The working groups and their laboratory leads played a

valuable role in the development of this roadmap.

Industry partners and stakeholders that are identified later in this report graciously volunteered their time

and effort to work with the GMLC project team. They provided valuable input and review comments on

various drafts of the report. They also attended multiple webinars, working group meetings, and industry

meetings to provide this industry perspective for the roadmap.

The GMLC has three sensor projects, of which the GMLC Sensing and Measurement Strategy is one. The

other two are Advanced Sensors, and Data Analysis and Machine Learning. Tom King, who is the lead

for all of these sensor projects, provided guidance to the project leads and team during the development of

this roadmap.

We would like to thank Alfonso Tarditi of ORNL’s Power and Energy Systems Group and Tim McIntyre

of ORNL’s Sensors and Embedded Systems Group for their technical reviews of the draft document.

Also, we would like to thank Deborah Counce of ORNL’s Technical Communications Group for her

thorough technical editing of the document, as well as Michael Gipple and Jennifer Bowman of NETL’s

Technical Writing and Multimedia group for formatting, technical review, and graphical support.

1 Review and Assessment of Sensing and Measurement Technology for Electric Grids, Devices Including

Communications and Data Analytics Requirements, ORNL/SPR-2018/956, December 2018, prepared by the GMLC

Sensing & Measurement Strategy Project (PI: D. Tom Rizy, Task Lead: Paul Ohodnicki) and posted on the GMLC

website at https://gridmod.labworks.org/resources.

1

1. EXECUTIVE SUMMARY OF THE KEY RECOMMENDATIONS

The Sensing and Measurement Strategy Project is a foundational effort of the Grid Modernization

Laboratory Consortium (GMLC), spurred by the greater need for observability of the electric power grid

in the future. The GMLC Sensing and Measurement Technology Roadmap Report was developed as a

collaboration across the Department of Energy (DOE) national laboratory system in close partnership

with key partners and stakeholders from industry, academia, and other relevant government organizations.

The intent of the Roadmap is to establish a set of goals and needs for sensing and measurement, identify a

set of specific technology solution recommendations anticipated to meet those goals, and lay out a path to

deliver those recommended solutions for meeting the goals of the Grid Modernization Initiative (GMI).

The GMLC Sensing and Measurement Roadmap effort has been carried out as an iterative process that

summarizes the current state of the art of sensor and measurement technology2, outlines existing gaps,

and points toward potential areas of need and opportunity for federal investment to make a significant

impact. The Roadmap can serve as a living document based upon regular updates and improvements (i.e.,

every few years) through ongoing stakeholder feedback and engagements in collaboration with DOE.

The Roadmap identifies a number of strategic focus areas and research thrusts spanning the areas of (1)

advanced sensing and measurement devices, (2) network communications, and (3) data management and

analytics that can meet the observability needs of current and future power systems. The Roadmap also

outlines a set of high-value use cases that can demonstrate tangible benefit and beneficial impact for the

broad range of new sensing and measurement technologies being developed and deployed. The Roadmap

also addresses crosscutting sensing and measurement issues and recommends a set of crosscutting support

efforts to accelerate the deployment, implementation, and impact of advanced sensing and measurement

technologies within the modern power system. Finally, the Roadmap reflects on a new architectural

definition for the modern grid called the “extended grid state” (EGS)3, which expands the reach of the

power system to all of the modern assets interconnecting with the power system, including renewable

energy sources, energy storage, electric vehicles, responsive loads, and others.

1.1 USES AND SENSING TECHNOLOGY

A number of gaps identified by the team involve (1) specific parameters that require improved visibility

through advanced sensor device technology development, (2) needs for enabling technology development

(e.g. low-cost manufacturing, sensor materials) to support the successful realization of advanced sensor

devices, and (3) characteristics needed by advanced sensor device technologies. Based on these identified

gaps, the team made a number of recommendations, around which a number of research thrusts were

identified. The following are specific areas of focus recommended for achieving targets set by federal

initiatives seeking to accomplish the goals of the grid modernization initiative. Direct digitally printed

passive wireless sensor technology (PWST) may address most. Detailed targets for specific technology

development efforts including performance and cost metrics as well as recommendations for

prioritization, are included within the body of the roadmap.

1. Dramatic reductions in cost for devices with similar performance to existing sensor devices, as

well as extremely low-cost sensing approaches with reduced but adequate overall performance to

enable wider deployment and greater system, particularly in distribution systems where lower




website at https://gridmod.labworks.org/resources. 3 Extended Grid State Definition Report, prepared by the GMLC Sensing & Measurement Strategy Project, PI: D.

Tom Rizy, Task Lead: Jeff Taft, Version 3.2 current draft, to be published as a PNNL and GMLC Report.

2

cost assets reside, with resultant benefits in terms of overall electrical system resiliency and

reliability. For example, ultra-low-cost, proxy-based sensing platforms (e.g., acoustics, vibration)

can serve as substitutes for direct monitoring of hard-to-measure parameters (e.g. partial

discharge).

2. High-temperature and harsh-environment sensing platforms for monitoring conventional generation

assets to improve reliability and efficiency in light of the needs for greater generation cycling

(ramping up/down of power output) in a modern power system with greater penetration of variable

renewable resources (wind and solar); both utility scale as well as distributed energy resources (DER)

primarily connected to distribution system.

3. Enabling materials and manufacturing technologies such as advanced sensor materials, new

packaging materials or techniques, and advanced manufacturing to enable new lower-cost sensing

platforms not currently possible with conventional manufacturing techniques.

4. Temperature and chemical (e.g., dissolved gas analysis or DGA) sensing approaches for internal

monitoring of electrical grid and generation assets for enhanced ability to predict incipient failures of

grid assets at the distribution level before they occur.

5. High-bandwidth, low-latency electrical parameter measurements, including frequency-selective

sensors for increased capability to identify abnormalities in electrical systems and assets quickly and

with sufficient time to enable dynamic protection schemes.

6. Sensor platforms that provide (a) multi-parameter capability, (b) compatibility with deployment

internal to electrical and generation assets, and (c) capability for spatially distributed measurements to

enable a suite of sensing technologies with optimized trade-offs in performance, cost, and spatial

characteristics. In this way the value of a given sensor placement can be matched with the associated

cost and value for deployment at the distribution or transmission level.

7. Wireless, self-powered, and/or passive, self-configuring, and self-calibrating sensors to enable future

transactive controls among other grid monitoring applications.

8. Optimal identification of new and existing weather monitoring infrastructures for advanced renewable

energy (including DER) forecasting and integration into system control centers, and extraction of

value streams from variable renewables to enhance resilience against natural disasters.

1.2 COMMUNICATION AND NETWORKS

Communication-related gaps could be clearly linked to (1) the need for optimized spectrum utilization

and ease of integration of new technology platforms into various communications networks, (2) overall

architecture characteristics, and (3) the need for standards as well as protocols for communication and

networking technology. Based on these identified gaps, the team made a number of recommendations,

around which a number of research thrusts were identified. The following are specific areas of focus

recommended for achieving targets set by federal initiatives seeking to accomplish the goals of the grid

modernization initiative. Detailed targets including performance and cost metrics as well as

recommendations for prioritization, are included within the body of the roadmap.

1. Design and develop a cost-effective, scalable communications fabric to support the wide range of

next-generation sensors, systems, and DER or DER components under investigation.

3

2. Design and continue to implement a distributed communications architecture that addresses the

challenges surrounding new technology developments, such as the Industrial Internet of Things and

5G wireless.

3. Develop a scalable, rapid speed, high-bandwidth, and low-latency communications network to

support cyber-secure transport of data associated with electrical parameter measurements.

4. Address spectrum utilization challenges through distributed scheduling schemes and distributed

intelligence, for example, as well as dynamic spectral resource allocation.

5. Quantify uncertainties and security risks of communication systems in the context of the modern

electric power system and develop self-healing and more robust capabilities to oppose malicious

operations in response to increasing concerns about cyber-physical security.

1.3 DATA MANAGEMENT AND ANALYTICS INCLUDING GRID MODELING

Gaps could be clearly linked to (1) data management, standards, and utilization as well as (2) data

analytics technique development, applications for grid modeling, operations and real-time security

assessments, and deployment. Based on these identified gaps, a number of recommendations were made

and a number of related research thrusts were identified. The following are specific areas of focus

recommended for achieving targets set by federal initiatives seeking to accomplish the goals of the grid

modernization initiative. Detailed targets including performance and cost metrics as well as

recommendations for prioritization, are included within the body of the roadmap.

1. Specifically focus data management in the utility sector on addressing three gaps: cost justification,

workforce education, and standardization.

2. Simplify human-machine interactions with advanced data management and analytical tools, both

visualization tools and user interfaces, to accelerate implementation by utilities, for example, by

engaging operators throughout the research and development process.

3. Standardize data formats and interfaces and develop and apply techniques for data quality monitoring

in real time. Consider a consortium for data standardization through the GMLC.

4. Develop and apply data analytics methods, including distributed data analytics, which enable the

coupling of spatially dispersed sensors of varying types to accomplish desired electric power system

objectives.

1.4 CROSSCUTTING SENSING AND MEASUREMENT SUPPORT

A clear need exists for foundational efforts to support the successful technology development and

deployment of advanced sensing and measurement tools and methodologies throughout the electrical grid

infrastructure. A recommendation is made to establish a Crosscutting Sensing and Measurement

Support effort that spans the various research thrusts and initiatives outlined in more detail in

subsequent sections of the Roadmap. The objective of this crosscutting effort is three-fold: (1) To raise

awareness of the identified issues that are common across different sensing and measurement areas. (2)

To create a gateway for stakeholders to efficiently access the right expertise and resources to address the

issues and to share lessons learned. (3) To provide necessary support, technical or nontechnical, to

facilitate the first two efforts. As a result, the crosscutting area should (1) provide a voice for the utility

industry regarding challenges it faces in deploying and leveraging new sensing and measurement

4

technologies, (2) provide tools to enable clear valuations of various sensing and measurement

technologies, and (3) collate and clarify the costs and reliability of existing and emerging solutions.

Based on the crosscutting issues and needs identified in the working group process, four crosscutting

initiatives are recommended:

1. Cyber-physical security awareness and support

2. Standards and testing to support improvement of sensor performance, reliability, resiliency, and

interoperability

3. Evaluation methods for determining valuation (costs, benefits, strengths) of sensing and measurement

technology

General crosscutting needs support for industry and utility partners in technology deployment

5

2. TECHNOLOGY ROADMAP ORGANIZATION

The remainder of the GMLC Sensing & Measurement Technology Roadmap (identified going forward in

the text as the Roadmap) is organized into the following sections:

• Background and Context

• Sensing and Measurement in the GMI

• GMLC Sensing and Measurement Strategy Process

• Sensor and Measurement Technology Roadmap Process

• Technology Review and Assessment Report Findings

• Working Group Gap Analysis Results Summary

• Crosscutting Sensing and Measurement Support

• High-Value Use Cases and the Extended Grid State Definition

• Key Findings and Federal Efforts to Address Gaps

• Proposed Research Thrusts Including Metrics

• Appendices A–E

More detailed information about the current state of the art in regard to sensing and measurement, as well

as existing programmatic activities and efforts, can be found in the Sensing and Measurement Technology

Review and Assessment Report.4




website at https://gridmod.labworks.org/resources.

6

3. BACKGROUND AND CONTEXT

Historically, the electric power grid has been a fully controlled system in which central generation

operated to delivery power via the transmission and distribution system to meet and follow the end-user

load; in that sense, therefore, the system was a highly predictable network. Electricity was generated in

centralized power generation plants and transported through high-voltage transmission and lower-voltage

distribution lines to end-use industrial, commercial, and residential customers. Utilities owned all of these

assets and managed generation output to follow the customer’s load demand and maintain system

frequency. Those times have changed significantly.

In recent years, the power system has become significantly more complicated, with more independent but

interdependent actors and changes in asset ownership. An increasing number of utility-scale generators

are now owned and operated by independent generation owners and operators. The mix of generation

technologies also has changed and is continuing to do so drastically, to include more renewable energy

sources and energy storage. Moreover, many customers no longer are just consumers of electricity but

also now own and/or operate small distributed generators connected to the distribution system.

Independent grid operators now operate competitive wholesale power markets and dispatch electric power

delivery operations in two-thirds of the nation. Natural gas–fired generation is displacing many coal

plants as a result of clean air and environmental concerns and displacing nuclear power plants because of

the high cost of completing and maintaining them. As a result, customer end-use demand, and even power

generation, is no longer as predictable as it used to be, because customer loads vary as a result of on-site

renewable generation and power generation at renewable power plants may fluctuate with weather

conditions. Operational challenges include more diverse and complicated components (such as

electronics, automated controls, renewable generation, and aging generation and power delivery assets),

and more complex, dynamic, hard-to-predict behaviors (such as power system oscillations). As a result,

grid conditions can change quickly, requiring better sensing and measurements along with associated

communications and data analytics and faster controls.

Power systems continue to be highly reliable but are now operating much closer to their operational limits

with lower reserve margins (i.e., 15%) for resource adequacy. The “power system,” in its beginning,

stretched from the power plant to the customer meter, including all assets in between. Today the power

system extends far beyond utility control. It is affected by factors including the proliferation of “smart,”

interconnected customer end-use devices (including electric vehicles; smart, thermostat-controlled

heating, ventilation, and air-conditioning systems; and energy management system–controlled end uses)

distributed generation and storage (e.g., solar photovoltaics, batteries, and back-up generators) throughout

the utility; and many non-utility generation and storage assets.

Customers, society, and the economy place very high demands upon the power system and deservedly so

—human, economic, and industrial health are highly dependent on highly reliable yet affordable grid

power. At the same time, many stakeholders demand and expect clean, sustainable energy sources to

ensure a clean environment. Furthermore, US dependence on reliable electric power will continue to

grow, with greater use of electricity for transportation via electric cars and mass transportation, and for

end-use consumer products including smart appliances and smart homes.

To meet these challenges, the US Department of Energy (DOE) launched the Grid Modernization

Initiative (GMI) to identify and respond to the needs of the “modern grid” as well as its operators and

planners. The GMI seeks to develop new grid architectural and planning concepts and new tools and

technologies to measure, analyze, predict, operate, manage, control, and automate power system

operations needed to transition to a smarter, reliable, resilent modern grid. The Grid Modernization

Laboratory Consortium (GMLC) is a collaboration involving hundreds of millions of dollars from DOE

to fund its national laboratories and industry in support of the GMI. DOE has funded the GMLC to

7

advance research, design, development, and applications to improve understanding of the power system

and provide the tools and technologies needed for the operation and planning of the modern grid. Through

the GMLC, DOE currently manages a large portfolio of research and development (R&D) projects in six

R&D areas, including

• grid operations

• devices and testing

• design and planning

• security and resilience

• system operations and control

• sensing and measurement

Each of these six R&D areas has an extensive R&D portfolio of activities intended to accelerate the

achievement of a resilient, secure, reliable, affordable, flexible, and sustainable modern grid.5 The focus

of this report is on the sensing and measurement area.

5 The reader is encouraged to review source material for more information about the GMI and GMLC, in particular

DOE’s Grid Modernization Multi-Year Program Plan (November 2015), https://www.energy.gov/downloads/grid-

modernization-multi-year-program-plan-mypp. That document lays out the changing demands upon the aging

current US power system and the changes needed to modernize the grid.

8

4. SENSING AND MEASUREMENT IN THE GMI

A major driver for GMI is the number of major past power system outages known to result from a lack of

adequate situational awareness of grid conditions. Threats to the power system also have multiplied,

including extreme weather events, cyber attacks, terrorist attacks, human errors, system errors, and aging

assets and infrastructure. To address this problem, a major focus for the GMI is to improve measurement

and monitoring of power system grid state and assets, in terms of performance health and capabilities.

Better information and situational awareness allows more efficient, effective, and flexible grid control and

operation and improves long-term, short-term, and real-time power system operational reliability and

resiliency. For this reason, the sensing and measurement area of the GMLC is a core, foundational effort

required to successfully realize the goals and objectives of the GMI and the modern grid.

The North American electric grid is going through a transformation to achieve GMI objectives of

reducing outage costs by 10%, operational costs of reserve margins by 33%, and DER integration costs by

50%6. The traditional electric grid was designed and operated as a “load following” power delivery

system with centralized generation that is controlled to meet and follow load demand and thus balance

load and generation to keep the frequency of the grid stable at 60 Hz. High reliability of grid operation is

achieved by monitoring assets, primarily at the generation and transmission level. Monitoring and control

assets at the distribution level primarily observe and control substation-level equipment and power

quality—including voltage and waveform distortions—to maintain viable voltage at the fundamental grid

frequency.

The emergence of DERs (e.g., solar photovoltaic, wind, energy storage) can make the grid resilient

through distributed control; but it also introduces new challenges for monitoring and control. To

maximize the benefits of DER, grid parameters must be monitored at the generation, transmission, and

distribution levels at a higher spatial and temporal resolution than ever before to ensure the safety of

operators and optimal control of the complete system.

The objective of the sensing and measurement effort is to develop and deploy novel and advanced sensors

at multiple levels of the grid in a cost-effective manner for rapid adoption. Currently, the power system

uses multiple layers of sensors (e.g., electrical, mechanical, chemical), transducers (potential and current

transformers), and actuators (e.g., breakers, capacitor banks, voltage regulators, reclosers). These sensors,

transducers, and actuators monitor and control power flow, voltage level, and power quality from

generation through the transmission and distribution (T&D) system to end loads. However, they are not

integrated; are used in a localized fashion, primarily because of communication challenges; and often are

expensive, especially for distribution systems, and thus are used only in niche applications. Thus, new

R&D is needed to overcome the various technical and economic challenges of advanced sensor

development, design, deployment and use.

Existing and emerging sensor solutions must balance three nonorthogonal dimensions of application,

integration, and cost:

1. Application requirements: These are dictated by the optimal resolution and accuracy needs to

support decision-making frameworks and applications in use by utilities and the broad range of other

electric grid stakeholders.

2. Integration requirements: These are dictated by utility operational, planning, and regulatory

frameworks with procedures for deploying new sensors into existing infrastructure with minimal

6 Identified in the Grid Modernization Multi-Year Program Plan [MYPP].

9

disruption to power system reliability. They must also be integrated and interoperable with existing

sensing, communications, and control infrastructures.

3. Cost requirements: The adoption of new technologies must be cost-effective throughout their entire

life cycle including installation, maintenance, and calibration. In particular, integration with legacy

electric grid assets drives sensor cost requirements, which differ at various levels of grid

infrastructure (e.g., monitoring generation assets vs. transmission assets vs. distribution assets vs.

end-use systems).

The increasing importance of power network cybersecurity requires new sensors to address cyber-

physical security. Detecting and mitigating complex cyber threats to the power grid and its assets is an

additional requirement that must be considered for new sensor installations.

Sensors represent both an opportunity and a risk for power system cybersecurity. On the positive side,

sensors are critical instruments for detecting and mitigating cybersecurity threats to power system

infrastructure. Sensors designed to measure and analyze communication systems are useful for intrusion

detection and intrusion prevention systems. Unfortunately, sensors are also vulnerable to cyberattacks,

including spoofing, denial of service, and man-in-the-middle attacks. A brief discussion of control system

architectures used to describe the interactions between operational technology (OT) and information

technology (IT) components of energy systems, as well as the intersection between advanced sensor

technologies and cybersecurity as it relates to the electric power system, is provided in Appendix B.

Within DOE, the Cybersecurity for Energy Delivery Systems Program’s roadmap for cybersecurity

provides a robust intersection with GMLC sensing and measurement activities.7

Balancing application, integration, cost, and cyber-physical security requirements drives innovation in

sensor development to meet targets in performance, cost, and deployment. Historically, sensor

development inherently included the device-transducer, embedded computing for data processing, and

end-to-end communication. Direct digital printing of passive wireless sensor technology (PWST) breaks

this mold by employing very low-cost, battery-free sensor designs, combined with interrogators for data

collection. The embedded computing is centralized at the interrogator rather than being replicated at every

sensor node. This emerging PWST technology holds promise for drastic cost reductions by eliminating

the two most costly elements of traditional sensor designs—embedded computing and the power source

required to do the computing.

These novel sensor technologies must be developed to be appropriately scalable and reliable for utility

adoption. By understanding vital parameters throughout the electric infrastructure, from generation

through end-use, utilities will be able to assess grid health in real time, predict behavior, and detect

potential disruptions; quickly respond to events; and better address future challenges. The key R&D

challenges are to (1) develop and demonstrate novel sensors that improve observability of the electric grid

at a very high resolution, and (2) use visibility to improve grid operation by reducing outages and

improving reliability.

A sensor device typically includes all or a subset of the following four elements:

1. A physical transducer that converts the physical parameter measured (measurand) to an electrical

signal for processing

7 Individuals interested in examining the CEDS-sponsored projects may wish to visit

https://energy.gov/oe/cybersecurity-energy-delivery-systems-ceds-fact-sheets where the individual fact sheets are

available.

https://energy.gov/oe/cybersecurity-energy-delivery-systems-ceds-fact-sheets

10

2. A computational device, typically a microprocessor or microcontroller, that converts the electrical

signal from the transducer to digital information

3. A communication device that transmits the information over a wired or wireless network to a location

for enabling data analytics and, ultimately, decision-making

4. A power supply or system that provides power to various elements of the sensor device

The convergence of the four key elements has occurred over decades to achieve a fully integrated sensor

or measurement system. In recent times, this convergence has accelerated, partly because of increasing

abilities for real-time data processing and analytics, along with consumer-grade manufacturability of

ultra-low-power digital circuitry. These revolutionary developments fueled the growth of a large number

of networked sensors called the “Internet of Things” (IoT). Information from these networked sensors has

driven data analytics applications that can monitor data from a substantial number of heterogeneous data

sources, infer complex underlying dynamics, present a diagnosis of the system behavior, and provide

situational understanding for operators to make informed decisions.

Innovation is required to develop fundamental sensor technology for improving grid operation

along with deployment strategies that reduce the total costs of sensor installation and

commissioning.

All four of the sensor elements listed exist in PWST networks. The difference is that, in PWST networks,

elements 2, 3, and 4 are found only in the interrogators and are not replicated at every sensor—

significantly reducing sensor costs. PWST also eliminates maintenance costs associated with battery

replacement. In addition, PWSTs can be bundled into multi-sensor modules.

A variety of sensors are used on distribution grids, and a key aspect of the sensing strategy is to determine

the mix of sensors needed to meet any specific set of smart grid outcomes. Table 1 lists and describes

some of the standard sensor types that fall within this definition.

Table 1. Common grid sensor types.

Sensor type Description

Faulted circuit

indicator

Provides a binary indication of the passage of a fault current (based on magnitude) past the

sensing point.

Line sensor Typically, samples voltage and/or current and provides various derived quantities, such as

root-mean squared (RMS) volts and/or amps, real and reactive power, power factor, a

limited number of harmonics (i.e., 3rd to 15th) of voltage or current, and total harmonic

distortion (THD). Transducers may be electrical, magnetic, or optical.

Phasor

measurement unit

Provides synchronized voltage and current synchrophasors (time synchronized by an

accurate time signal, such as global positioning system or GPS), frequency, and rate of

change of frequency. May also provide line power flows, breaker status, or other analog

and/or digital values.

Sag sensor Measures conductor sag (droop) in transmission lines. Transducers include cable tension

meters and video camera/target approaches.

Sway/aeolian

vibration

Measures wind-induced sway (conductor swing) and vibration in transmission lines.

Snow/ice loading Measures snow/ice load on power lines during winter conditions.

Dissolved gas Measures dissolved oil gas concentrations for up to nine gases in power transformers; may

compute metrics of transformer health.

Partial discharge Detects and counts arcing partial discharges in power transformers.

11

Cable tan delta Measures phase shift on cable insulation.

Bushing capacitance Measures capacitance on power transformer and breaker bushings.

Table 1. Common grid sensor types (continued).

Sensor type Description

Line temperature Measures temperature distributions on power lines—typically done with fiber optics.

Residential meter In addition to electricity (kWh) usage (energy), may measure secondary voltage; may

record data on voltage sags as measured on the secondary at the premise; a few also record

real and reactive power and power quality measures, such as voltage THD.

Commercial and

Industrial (C&I)

meter

In addition to electricity (kilowatt-hour or kWh) usage (energy), measures secondary

voltage and current, computes real and reactive power, THD, and a variety of other

configurable quantities. May capture power waveforms on a trigger basis for later retrieval.

Feeder meter Provides meter-quality measurement of feeder primary quantities, including voltage,

current, and real and reactive power.

Digital fault

recorder

Captures and stores voltage and current waveforms upon the occurrence of an event (i.e.,

short-circuit fault) triggering.

Winding hotspot

monitor

Monitors transformer temperatures (in the windings) and estimates hot spot temperature.

Tap changer

monitor

Counts tap changer operations that increase/decrease downstream voltage. May also detect

arcing and capture electrical and vibration signatures during tap changes.

Many modern distribution automation devices include a sensing capability as either an integral part of, or

in addition to, their primary functions. Table 2 lists some examples of common grid devices that can also

provide an integrated grid sensing functionality.

Table 2. Common grid devices with sensing capability.

Device Sensing capability

Switch controller Measures voltage; may record peak fault currents.

Capacitor controller Measures voltage; may record peak fault currents; may compute real and reactive

power.

Recloser controller Measures voltage; may record peak fault currents.

Voltage regulator Measures line voltage.

Substation intelligent

electronic devices

(microprocessor relays)

Can take transducer inputs for voltage and current directly; can compute many

derived values, including real and reactive power, phasors, total harmonic distortion,

power factor. Also acts as a gateway for other kinds of measurements, such as oil

temperature and partial discharge data.

12

5. GMLC SENSING AND MEASUREMENT STRATEGY PROJECT

The GMLC Sensing and Measurement initiative is organized into three project areas that have strong ties

and interfaces with many other actively funded GMLC projects (Figure 1).8 The Advanced Sensors

Project works to develop new sensors to meet the needs of the modern grid. The Data Analytics and

Machine Learning Project seeks to identify gaps in data analytics for the modern grid and develop and

apply machine learning as well as other analytics algorithms to turn sensor data into useful information to

meet modern grid objectives. The Sensing and Measurement Strategy Project is developing an overall

strategy for sensing and measurement, including identifying grid states, and determining sensors needed

for applications, as well as determining communication requirements, and data management and analytics

needs. This roadmap report is a product of the GMLC Sensing and Measurement Strategy Project.

Figure 1. Overall graphical representation of the GMLC sensing and measurement area. Core foundational

projects are illustrated with bold lines and placed in the context of other related activities. Note: INL’s role is

called out specifically since they were not part of the project team but participated in a voluntary fashion to

provide input on the communications roadmap developed for DOE under another project.

The GMLC Sensing and Measurement Strategy effort began in April 2016. It focuses on defining

measurement parameters within the power system, devices for making these measurements,

communication to efficiently transport these data to where they are needed, and data analytics to

effectively manage the data and turn them into actionable information for operational and planning

decisions. The following are the project objectives:

• Task 1: Create an extended grid state (EGS) reference model that extends beyond the traditional

“T&D system” definition and framework, identifying the information needed to understand how to

8 See the following links for more information: https://gridmod.labworks.org/projects/, https://energy.gov/under-secretary-

science-and-energy/doe-grid-modernization-laboratory-consortium-gmlc-awards,

https://www.energy.gov/under-secretary-science-and-energy/grid-modernization-lab-consortium

https://www.energy.gov/under-secretary-science-and-energy/grid-modernization-lab-consortium

13

instrument the extended electric grid that includes renewables, responsive load, and other new

technologies.

• Task 2: Develop a technology roadmap to drive development of sensing and measurement

technologies needed to measure electric grid parameters, including quantitative metrics where

applicable.

• Task 3: Develop a sensor placement optimization tool (SPOT) that supports the selection and

allocation of sensors to achieve the best possible levels of observability subject to the reality of

constraints on practical sensor placement and installation.

• Task 4: Conduct outreach to standards development organizations and technical groups to coordinate

with industry to achieve its participation in the project, ensure industry acceptance, and identify

standards (new and enhancements).

• Develop a test bed or beds for sensor qualification and certification. Such test beds would help

developers of new sensor technology verify compatibility with the field environment and provide

direct feedback from end users. Multiple use cases could be developed to test sensors, validate their

integration into existing plant infrastructure, and more.

Figure 2 shows a graphical summary of the overall GMLC Sensing and Measurement Strategy project. It

illustrates the EGS providing an overarching framework. It is then combined with a sensor technology

roadmap as well as sensor placement and optimization tools to clarify the needs for advanced sensing and

measurement technologies to support the GMI in the future.

Figure 2. A graphical summary of the overall GMLC Sensing and Measurement Strategy project.

14

The Sensing and Measurement Strategy is being carried out by a large cross-laboratory team with active

participation from ten of the DOE national laboratories:

• Oak Ridge National Laboratory (ORNL) is the lead for the project and tasks 3 and 4.

• National Energy Technology Laboratory (NETL) is the plus one (??) for the project and task 2 lead.

• Pacific Northwest National Laboratory (PNNL) is the lead for task 1.

• National Renewable Energy Laboratory (NREL)

• Sandia National Laboratories (SNL)

• Argonne National Laboratory (ANL)

• Lawrence Berkeley National Laboratory (LBNL)

• Lawrence Livermore National Laboratory (LLNL)

• Los Alamos National Laboratory (LANL)

• Idaho National Laboratory (INL)

Historically, grid monitoring has been used primarily for bulk electric power (generation and

transmission) assets with little or no visibility at the distribution level, because monitoring devices have

been costly to acquire and deploy and communications and analytical capabilities were limited and

expensive to implement. But recent technology advances in materials science, electronics, photonics,

communications, and advanced manufacturing have opened up new possibilities for the realization of

cost-effective measurement and monitoring in every part of the power system. At the same time, advances

in data storage and management, data analytics, and two-way control systems make it possible to use

collected grid data to better control and manage the power system at all levels.

The technology advances making it possible to develop better sensing, measurement, communications

and analytical capabilities include

• Additive manufacturing using functional materials in addition to structural materials to make novel

sensors and embedded sensors inside or on items that are being monitored.

• PWST that enable ultra-low cost sensors.

• Network architectures that include fixed, man-portable, or mobile drone data collection. Using PWST

does not place the intelligence at the edge but rather pulls it one step back at the interrogator. The

intelligence is localized, but not all the way to the edge, significantly reducing up-front sensor cost

and maintenance cost (no battery replacement).

• A wide suite of IT and communications (ITC) advances, including affordable high-speed

communication networks, solid-state measurement and analysis on a chip, and high-density data

storage and management capabilities.

• The convergence of measurement and control functionality into multi-function, multi-purpose

measurement and control devices.

• Decentralization of analytics and controls out to the grid edge (closer to customer end-use locations

and distributed generation injections), rather than sending all of the data back to a central hub for

analysis. This enables more timely decision-making and action and multi-directional communications

and controls.

15

• Big data analytics to recognize event signature patterns in very large data sets, diagnose power system

problems and determine solutions from these data, improve asset management, and identify real-time

operational threats and solutions gained from insights extracted from these data.

• The widespread use of technical standards and interoperability to enhance the interchangeability,

coordination, availability, performance quality, and capabilities and lower the costs of sensing and

measurement devices.

• Leveraging the use of ITC and analytics in many other industries and sectors to solve problems like

those of the electric power sector. The electric power sector can look for solutions to analogous

problems developed by the military (e.g., field force management and operational sector threat

awareness), manufacturing (e.g., quality control monitoring in aircraft and semiconductor

manufacturing), businesses (e.g., integration of diverse data for trending using machine learning and

big data analytics), banking and finance (e.g., high-reliability communication and data quality and

security management), and health care (e.g., use of surrogate, noninvasive, easy-to-monitor

techniques such as acoustics and vibration to monitor hard-to-measure variables).

The Sensing and Measurement Strategy project team has considered these advances and others. The

Technology Roadmap provides a suggested set of initiatives and research thrusts for a coherent,

integrated, coordinated government and industry strategy for technology development and deployment in

support of GMI goals. This document represents the current version of the Sensing and Measurement

Technology Roadmap for the Sensing and Measurements Technical Area of the GMI. The Technology

Roadmap is a living document that should be updated regularly by roadmap stakeholders.

16

6. SENSOR AND MEASUREMENT TECHNOLOGY ROADMAP PROCESS

This Technology Roadmap development effort seeks to accomplish the following objectives in support of

DOE’s GMI9:

1. Identify a clear understanding of the current state of the art in sensing and measurement devices,

communications, and data management/analytics as it relates to the electric power system, spanning

electricity generation, transmission, distribution, and ultimately end users.

2. Perform a gap analysis of sensing and measurement technology needs compared with the current state

of the art.

3. Articulate the needed visibility to enable a modernized electricity grid infrastructure as outlined in the

GMI Multi-Year Program Plan (MYPP).10

4. Develop a prioritized technology roadmap with recommendations for R&D in sensing and

measurement.

5. Establish new, urgent, and targeted federal funding to support initiatives that accomplish the ultimate

GMI objectives.

The GMLC Technology Roadmap has been developed as a collaboration across the DOE national

laboratory system in close partnership with key partners and stakeholders from industry, academia, and

other relevant government organizations. An abbreviated list of major participating stakeholder partners

can be found in Figure 3. A more complete and detailed list of participating organizations and individuals

is provided in Appendix D.

9 http://energy.gov/under-secretary-science-and-energy/grid-modernization-initiative 10 http://energy.gov/downloads/grid-modernization-multi-year-program-plan-mypp

http://energy.gov/under-secretary-science-and-energy/grid-modernization-initiative

http://energy.gov/downloads/grid-modernization-multi-year-program-plan-mypp

17

Figure 3. Summary of various stakeholders involved in the Sensing and Measurement Strategy.

The Technology Roadmap effort has been carried out as an iterative process: (1) summarize the current

state of the art, (2) outline existing gaps, and(3) identify areas of potential need and opportunity for

federal investment to make a significant impact.

18

The first phase of the roadmap process began with the development of an extended literature review by

the national laboratory team that was subsequently updated in later stages of the project.11 The result of

this effort was a Technology Review and Assessment document that contains information on previous

roadmaps, technical literature, program documents, and other resources used for the roadmapping effort.

A first draft of the Technology Roadmap without detailed gap analysis or prioritization was presented to

stakeholders in a public industry meeting held at ComEd in February 2017 to garner initial stakeholder

feedback to inform the path forward. A revised draft of the Technology Roadmap slides was provided to

DOE program managers for review and input in April of 2017.

The second phase of the process began in August 2017 with the goals of (1) improving the integration of

the EGS definition with the Technology Roadmap; (2) engaging with stakeholders to refine the proposed

research thrusts and perform a detailed gap analysis, including the development of quantitative metrics;

and (3) developing a set of specific, actionable recommendations for federal initiatives that could advance

the GMI objectives. The Sensing and Measurement Strategy project team established several working

groups to coordinate and accomplish each of these primary objectives (see further details in Appendix C).

These working groups consisted of national laboratory personnel and industry members. Each of these

working groups operated independently, with oversight and coordination by the Sensing and

Measurement Strategy project principal investigator and roadmapping task lead. More details of the

roadmapping process, including the list of working group leads and summary reports from each working

group, can be found in Appendix C.

The Technology Roadmap offers recommendations to achieve a coherent, integrated approach toward the

development and deployment of new sensing and measurement technologies in support of GMI goals and

objectives. A number of strategic focus areas and research thrusts have been identified, spanning the areas

of (1) advanced sensing devices, (2) network communications, and (3) data management and analytics

solutions that can meet the observability needs12 of the current and future power system.13 A set of high-

value use cases is also presented, which can demonstrate tangible value and beneficial impact for the

broad range of new sensing and measurement technologies being developed and deployed. A set of

crosscutting sensing and measurement support efforts are also identified and recommended to accelerate

the deployment, implementation, and impact of advanced sensing and measurement technologies within

the modern power system.

The Technology Roadmap also reflects a new architectural definition for the modern grid, the EGS.14 The

EGS offers a common framework and description for the modern power system beyond just the cables,




website at https://gridmod.labworks.org/resources. 12 The GMI work recognizes that the power system is dynamic and requires “temporal, geospatial and topological

awareness of all grid variables and assets.” (Extended Grid State Definition Report, prepared by the GMLC Sensing

& Measurement Strategy Project, PI: D. Tom Rizy, Task Lead: Jeff Taft, Version 3.2 current draft, to be published

as a PNNL and GMLC Report.). Such observability enables “visibility,” estimation and forecasting of the power

system, and therefore better situational awareness of current conditions and contingencies. 13 The term “power system” refers to the entire scope of the electricity delivery system: generation, T&D, customer

end uses and customer-owned electric production and storage devices, and all of the control and decision-making

actors and activities along that thread (including energy management systems, automated systems, price- and

market-responsive actions, and demand-response programs). The term “grid” is used to refer to only those elements

that are located on the supply side of the meter, including generation, transmission, and distribution elements and

communication networks. 14 Extended Grid State Definition Report, prepared by the GMLC Sensing & Measurement Strategy Project, PI: D.

Tom Rizy, Task Lead: Jeff Taft, Version 3.2 current draft, to be published as a PNNL and GMLC Report.

19

conductors, and other electrical assets that make up the electrical transmission and delivery system. The

EGS includes the connected topologies and interactions for the following:

• Grid operations and control hierarchy and topologies

• All the grid’s assets and components

• Communications and analytical systems

• Electrical conditions such as consumption, generation, fuel mix, electricity uses, and system

performance, including specific electrical measurements

• Energy markets

• Ambient state external conditions that affect the power system, including weather and external

operational constraints such as environmental emissions rules, North American Electricity Reliability

Corporation (NERC) reliability standards, and a variety of dispatch and market rules

The latest version of the EGS definition15 provides more detailed information and references. The

graphical representation of the EGS is provided in Figure 4. This illustration is used to visualize

intersections between the EGS and the high-value use cases identified in subsequent sections.

Through the team’s efforts, focus areas have been identified for organization of the proposed Roadmap

R&D efforts. Because of the complex, interconnected nature of the EGS and the modern electric power

system, there is necessarily some degree of overlap among these focus areas. However, the focus area

framework helps the Technology Roadmap team organize and present roadmap findings and

recommendations while linking specific proposed research thrusts to broader emergent needs.

These focus areas are

• Crosscutting research that is needed to support the success of the sensing and measurement strategy

• Sensing and measurement devices

• Harsh environment sensors for flexible generation

• Grid asset health performance monitoring

• Phasor measurement units (PMUs) for grid state and power flow

• Novel electrical parameter transducers

• End-use/building monitoring

• Sensors for weather monitoring and forecasting

• Communication

– Distributed communication architectures

– Communications and networking technologies

• Data, analytics, and modeling

• Big data management for accessibility and visibility

• Analytics support and integration

• Advanced data analytics techniques, and applications

• Weather data for grid modernization

15 Extended Grid State Definition Document, prepared by the GMLC Sensing & Measurement Strategy Project, PI:

D. Tom Rizy, Task Lead: Jeff Taft, Version 3.2 current draft, to be published as a PNNL and GMLC Report.

20

The Sensing and Measurement Strategy’s Technology Roadmap should evolve as additional information

becomes available.

21

Figure 4. The taxonomy of the extended grid state16.

16 Extended Grid State Definition Document, prepared by the GMLC Sensing & Measurement Strategy Project, PI: D. Tom Rizy, Task Lead: Jeff Taft, Version

3.2 current draft, to be published as a PNNL and GMLC Report.

22

7. TECHNOLOGY REVIEW AND ASSESSMENT DOCUMENT FINDINGS

The summary of findings in this section represents a high-level overview of what was learned from the

development of a technology review in support of the phase of objective 1 outlined above in Section 6 in

terms of the review of the state of the art of sensors.17

The discussion of sensing and measurement devices in the Technology Review and Assessment Report18

was segmented into distinct application domains related to needs for the electric power system of the

future:

• Conventional generation sensing for more flexible operation

• Renewable generation sensing and weather monitoring

• T&D power flow and grid state monitoring

• Asset monitoring and fault diagnosis

• End use/buildings monitoring for more responsive loads

There is necessarily some overlap between these domains, and they are expected to become even less

distinct as the power system evolves to become more integrated and diverse. Nevertheless, sensing and

measurement approaches and technologies needed to address these application areas are sufficiently

distinct to organize focused efforts. The EGS definition has been developed under a parallel project

activity. 19

Useful insights about the current technology status within the application areas identified in the

Technology Review and Assessment Report and emerging needs in sensing and measurement devices are

summarized in the remainder of this section. They have been categorized into four areas in terms of

crosscutting needs, sensing & measurement applications, communication requirements, and data

management & analytics needs.

Emergent themes that crosscut the application areas

1. Needs exist for advanced instrumentation at centralized generation and transmission levels.

2. There is a lack of visibility within the distribution system.

3. The per device” value of a sensor deployed on the distribution system or at the end-user level is

dramatically lower than the value of comparable transmission system sensor.

4. Enhancing visibility in the distribution system and at the end uses requires advances in low-cost/value

added sensors and in multifunction or multi-parameter sensors.

5. There are obvious needs for clearer definitions and standardization of requirements.




website at https://gridmod.labworks.org/resources. 18 Review and Assessment of Sensing and Measurement Technology for Electric Grids, Devices Including



website at https://gridmod.labworks.org/resources. 19 Task 1 of Project 1.2.5 GMLC Sensing and Measurement Strategy Project.

23

6. Testing procedures for emerging sensor and measurement technologies are lacking. Tests of interest

include:

a. Interoperability

b. Cyber-physical security

c. Resilience of new technologies

7. Standards and testing procedures are important aspects of the development and deployment of new

sensing and measurement devices.

8. Valuation or value added is among the most important concerns and needs of utilities in making

decisions regarding a sensor deployment project. However, it is an intricate problem consisting of

many elements, including:

a. Cost/benefit analysis not only of the cost of devices but also of installation

b. Approval

c. Reliability impact and reliability/life cycle of the equipment

d. Regulatory risk assessment

There may be other considerations, such as the need for well-designed and validated evaluation

tools/methods that will encourage the adoption and deployment of new sensor and measurement

technologies, especially the emerging ones.

Key insights derived for the various sensing & measurement application domains

1. Harsh-environment instrumentation relevant for conventional thermal-based generators (e.g., fossil,

nuclear) could enable more flexible operation and minimize long-term impacts of cycling and

ramping on plant longevity and efficiency. Capabilities of existing automatic generation controllers

(AGC) and associated sensing and measurement devices should be evaluated in terms of the potential

for new technology innovations.

2. Weather monitoring technologies and instrumentation exist at high technology readiness levels, and

emerging technologies often involve adaptation of technologies developed for other fields, such as

unmanned aerial vehicles, lidar-based techniques, and satellite-based remote sensing. Additional

needs include (1) developing low-cost sensing options for scalable deployment of weather sensors

and enhanced grid-edge visibility; (2) integrating, calibrating, customizing emerging innovative

technologies for grid operational purposes; (3) developing high-quality and portable calibration

technologies; (4) achieving optimal deployment and usage of disparate weather-sensing resources for

modeling complex weather phenomena for challenging terrains and severe weather events and for

forecasting renewable generation at higher temporal and spatial resolutions (i.e., not just average

forecasts, but capturing the uncertainties accurately in terms of probabilistic forecasts); and

(5) effectively integrating them into energy management systems and distribution management

systems for enhanced situational awareness and achieving a high level of system performances (i.e.,

lean reserves).

3. PMUs are a key technology for power flow and grid state monitoring; and opportunities exist for

improvements in reliability, speed, accuracy, overall cost, especially for applications at the

distribution level. Emerging electromagnetic phenomena–based current and voltage transducers show

significant opportunity for new innovations but require reductions in cost.

4. Asset monitoring of electrical grid assets can be classified into both “functional performance” and

“health monitoring.” The former requires predominantly electrical parameter sensors and the latter

requiring sensors for a broad range of parameters, such as temperature, chemistry, and strain. Sensor

instrumentation exists for established grid components, but high costs currently limit deployment to

24

the most critical assets. New sensing technologies are required for emerging grid components, such as

power electronic-based solid-state transformers.

5. Trends of increased generation at residential and commercial scale, as well as projections for

widespread electric vehicle deployment require increased visibility both in the distribution system and

near or at the loads to enable demand response and transactive energy strategies. Low-cost sensor

technologies for monitoring power flow as well as parameters characteristic of the current and

forecasted load will be of increasing importance.

Conclusions related to communication needs

1. A paradigm shift is occurring toward broader implementation of distributed, rather than centralized,

architectures characterized by communication and intelligence at lower levels closer to the sensing

and measurement devices.

2. Reduced latencies and robust peer-to-peer communication and communication between various nodes

(or measurement points out on the power system) and the control center are of increasing importance.

3. Communication architectures with the following attributes are highly desirable:

a. Scalability to allow for managing many diverse sensing and measurement networks of varying

sizes.

b. Flexibility to incorporate new types of data and applications.

c. Efficiency in leveraging unique features of different communication technologies.

d. Reduced latency with more distributed data processing and control.

e. Reduced vulnerability to cyberattacks.

Conclusions related to data management and analytics needs

1. The desire for dramatically increased visibility across the electricity grid infrastructure will intensify

the demand for the deployment of sensing and measurement devices, and its associated data

management needs, to unprecedented levels.

2. A shift toward distributed data analytics methodologies rather than centralized approaches is a

potential key piece of the required technical solution.

3. For the existing sensing and measurement infrastructure, a great amount of value has yet to be

extracted through advanced data management and analytics approaches. This is especially the case at

the distribution level, which has traditionally been limited to substation monitoring and control with

very little to none on the distribution feeders.

The findings reported in this section have served as key inputs to the approach and organizational

structure of the Roadmap. Through a formalized working group process and stakeholder engagements, the

team has further developed and refined these early concepts and has also performed a detailed gap

analysis, developed potential recommended research thrusts, and identified crosscutting initiatives. These

findings have led to recommendations for federal efforts that can help to promote the goals of the GMI, in

the sensing and measurement area. Key findings of this formal working group process are described in the

following section.

25

8. WORKING GROUP GAP ANALYSIS RESULTS SUMMARY

Each DOE laboratory working group lead was asked to develop a team consisting of members from the

DOE laboratory system, industry, and other relevant organizations as required to accomplish a defined

objective related to advancing the Roadmap effort. The approach, results, findings, and recommendations

from each working group are presented as working group summary reports in Appendix D. This section

summarizes the primary results and recommendations of the formal working group process at an elevated

level, as described in the introduction section. Also, the results are integrated into an overall capability

analysis.

A “capability gap” is defined here as a deficiency such as performance (e.g. precision, repeatability,

reliability) in existing sensing and measurement technologies. Alternatively, the capability gap can refer

to a gap in the surrounding institutional frameworks, regulations, or standards needed to support the

objectives of the DOE GMI for the sensing and measurement area. All identified gaps and suggested

approaches to address these gaps—including the pursuit of new research thrusts, establishment of targeted

crosscutting initiatives, and other recommendations—are presented from these two perspectives. Because

the Roadmap effort was also established in parallel with the announcement of a major investment in new

initiatives across DOE to support the mission of the GMI, the Roadmap is written so that existence of

ongoing activities to address identified gaps within the GMI/GMLC portfolio does not preclude them

from being included in the document. The team has identified and mapped linkages with existing

activities and efforts in the working group summary reports presented in Appendix D.

Tables 3–5 in this section and Table 6 in Section 9 are summary tables outlining key capability gaps and a

summary of overall team findings organized as follows:

1. Uses and sensing technology targets

2. Communications and networks

3. Data management and analytics including grid modeling

4. Crosscutting issues

8.1 USES AND SENSING TECHNOLOGY TARGETS

Many capability gaps can be clearly linked to (1) specific parameters that require improved visibility

through advanced sensor device technology development, (2) needs for development of enabling

technologies to support the successful realization of advanced sensor devices, and (3) characteristics of

advanced sensor device technologies. These gaps are grouped in Table 3 within the subcategories Issues

to monitor, Advanced materials and techniques, and Needed advancements.

8.1.1 Issues to Monitor

Several specific parameters were identified as being relevant for the broad range of grid sensing

applications. In the case of asset health monitoring, a number of emergent opportunities were identified

including (1) proxy sensors, such as vibration or acoustic sensors external to a grid asset that can indicate

faults or failures that are otherwise difficult to measure directly, (2) tilt sensors to monitor utility pole and

line orientations relative to their vertical and horizontal directions, (3) internal parameter measurements

within electrical grid and thermal generator assets, such as temperature and chemistry, and (4) electrical

parameter measurements, including high frequency/bandwidth and frequency-selective responses.

Electrical parameters and proxy sensors were identified as particularly suitable for detection of low-

frequency but high-consequence faults or failures due to natural or human-caused threats to the modern

electric power system. Electrical parameter sensors focused on characteristic frequency bands may

become increasingly important for application within emerging electric power system technologies, such

26

as solid-state transformers and energy storage. Internal temperature measurements and internal chemistry

measurements, in contrast, can be used to identify the onset of failures due to natural aging or higher

frequency but less acute disturbances. Tilt sensors for utility poles and lines can enable more rapid

response times in cases where a power system infrastructure failure has occurred. They can also clearly

indicate locations where inspections should be conducted to proactively avoid the potential for costly and

disruptive system disturbances.

8.1.2 Advanced Materials and Techniques

In support of the development of new sensing platforms with ideal characteristics for modern power grid

applications, many enabling technologies and developments have been identified that should be pursued

in conjunction with the development of novel sensor devices. More specifically, there is a need for

advanced sensing material R&D to support the need for sensor transducing elements with optimal

characteristics for a particular application requirement. Functional sensing materials can be integrated

within various sensing platforms. Engineered materials can greatly simplify the cost and complexity of a

sensor device. There is also a need for advanced packaging and sensor device approaches and materials

that are compatible with both the electric power system and thermal generator application environments.

New low-cost, scalable manufacturing approaches have potential for significant impact on overall sensor

cost. Advanced methods including additive manufacturing, integrated circuit processing, advanced

photonic-based manufacturing methods, and roll-to-roll manufacturing techniques should be considered.

8.1.3 Needed Advancements

Several key attributes are required for emerging sensor technologies to have a significant impact on the

successful realization of the GMI objectives. One major consideration is the balance of trade-offs between

(1) the cost of a device and its deployment and (2) the value of the sensing technology to the owner of the

asset in question. The overall cost of the sensor deployment will ultimately dictate whether a particular

technology can be deployed ubiquitously or must be reserved for monitoring only the most critical assets

within the electric power system. Note also that in many cases, there is a disconnect between the value of

a new sensing technology in terms of its contributions to overall electric power system stability, and the

local value that the owner of the asset in question can extract. Many sensing and measurement

technologies already exist and are widely deployed across the electric power system. However, there is a

disconnect between organizations responsible for covering the full costs of deployment, and the full

system-level value of new sensing and measurement technology ubiquitously deployed across the system.

Therefore, it is not anticipated that the private sector alone will lower the costs of new sensing

technologies to the desired price point for accomplishing GMI objectives. Thus, significant capability

gaps exist with regard to (1) dramatic cost reductions for existing sensor technology platforms with

similar performance and (2) development of ultra-low-cost sensing technologies with reduced but

acceptable levels of technical performance. In addition to major cost reductions, there are also significant

opportunities to focus investments on a limited number of flexible sensing platform technologies that can

be tailored for a broad range of electric power system monitoring applications through (1) multi-

parameter functionality, (2) passive or “power-free” operation at the sensing node, and (3) optimized

spatial deployment strategies for monitoring a specific parameter of interest. Examples include linear

position sensors for power line sag monitoring, areal imaging for larger grid assets, substations with a

high density of grid assets, and point sensors for widely distributed assets.

Table 3 provides more details regarding these gaps and potential approaches to addressing them.

27

Table 3. Gap analysis summary for uses of sensing and technology targets.

Gaps identified by working groups Working groups Potential approaches to address the gaps

Issues to monitor

Nontraditional proxy sensors

Nontraditional but readily queried proxy sensors

can be deployed for early detection of fault

conditions

Asset health

Develop low-cost proxy sensors that can be

ubiquitously applied to grid assets, including

acoustic and ultrasonic vibration monitoring

Utility pole and line orientation at

distribution level

Local monitoring of utility pole and line

orientation, relative to true vertical and

horizontal orientation, can enable prevention of

failures and more rapid recovery and restoration

times

Asset health

Develop low-cost tilt sensors for poles and

lines that can be ubiquitously applied to grid

assets

Advanced thermometry for internal grid

asset monitoring

Thermal signatures are a primary indicator of

grid asset (e.g., transformer) health status

leading to faults and failures. However, internal

faults exhibit characteristic hot spots that can be

difficult to detect

Asset health

Novel

transducers

Develop multipoint temperature sensor

technologies and extremely low-cost single-

point sensor technologies for improved asset

monitoring

Internal chemistry monitoring for grid assets

Dissolved gas analysis (DGA) plays a key role

in asset health monitoring of transformers but it

is cost-prohibitive to make frequent

measurements. Thus, measurements are only

done periodically. Real-time measurement

systems are deployed at only the most critical

assets

Asset health

Develop real-time online DGA technologies

of varying performance for specific

application ranges and at dramatically

reduced costs. For example, leveraging

emerging sensor technology platforms rather

than accurate but costly direct spectroscopic

monitoring techniques should be explored

Internal generator parameters for flexible

operation

Existing generation plant monitoring will

become increasingly important because more

flexible operation is needed to accommodate

intermittent renewable deployments in a modern

grid

Asset health

Harsh

environment

Address specific metrics identified around

the needs of internal monitoring of

centralized generators. A specific research

thrust was identified for boiler water

chemistry monitoring based on industry input

Rapid electrical parameter sensing for

dynamic protection

Electrical parameters provided by fast-acting

and broadband sensors can provide most rapid

signatures of low-probability, high-consequence

events, such as human or natural threats (e.g.,

geomagnetic disturbance, electromagnetic

pulse). They can also play a key role in dynamic

system protection and offer a better

understanding of dynamic operating states

Asset health

Novel

transducers

Develop rapid high-bandwidth and low-

latency electrical parameter sensors with

sufficiently low cost for ubiquitous

deployment

Develop a new set of transducers capable of

providing information about rates of changes

(dynamic) of voltage, current, and frequency

28

Table 3. Gap analysis summary for uses of sensing and technology targets (continued).


Electrical parameter sensors for asset health

/performance

Electrical parameter sensors can be used to gain

information about the asset health and

performance of existing grid devices. Such

sensors are expected to be even more important

in the future for emerging technologies such as

next-generation (solid-state) transformers

Abnormal behavior (e.g., failures, faults, or

severe degradation of performance of an asset)

manifests itself in a deviation from nominal

operating frequency or presence of abnormal

frequencies (such as new harmonics or

completely new frequency characteristics).

Detecting such frequencies is key to the early

identification of faults and failures in assets

across the modern power system

Novel

transducers

Develop new sensors capable of providing

accurate information on frequency signature

and total harmonic distortion (THD) as well

as voltage and current at a price point that is

cost effective for multiple asset monitoring.

Emphasize low-cost solutions that can deploy

directly at/on the asset to be monitored to

complement approaches that seek to leverage

analytics combined with non-local PMU-

based monitoring solutions

End use-level sensors for leveraging IoT

devices

Sensors creating actionable information from

new smart internet-capable appliances and

devices installed behind the meter (customer)

location are not ubiquitous

Novel

transducers

Develop sensor solutions that monitor the

performance of a variety of devices at the

customer level and broadcast this information

to the utility. Examples include “smart

outlets” that can collect power and power

quality information, and smart meters that

provide revenue information, power, and

power quality information for all devices at

the customer’s interconnection

Enhanced visibility of weather-dependent

resources

With increasing penetrations of wind- and solar-

dependent energy sources, both at the

transmission and distribution (behind the meter)

levels, utilities and energy management systems

have increasing needs for higher temporal and

spatial resolution visibility of device statuses

and their expected power generation. This will

provide system operators with situational

awareness for making timely decisions. It will

also enable reliable integration of variable

renewables and efficient management of their

power ramps for grid reliability and resilience

Weather

Develop mesonets, weather stations, and sky

camera devices that provide high resolution

(<1 km spatial and seconds-minutes time

intervals), real-time, on-demand weather

information

Develop visualization technologies that

provide near-real-time situational awareness

of renewable devices as well as associated

grid states

Integrate satellite sensing data with ground-

mounted or drone mobile sensors to achieve

higher spatial and temporal resolution

29



Advanced materials and techniques

Enabling materials for sensing elements

Advanced materials development plays a critical

enabling role for new sensing elements in a

variety of sensor devices

Harsh

environment

Pursue foundational advanced sensing

materials research and engineering to provide

specific application requirements, which may

include deployment within internal grid

assets or within centralized thermal

generators

Enabling materials for harsh environment

sensors

Robust packaging technologies are required to

ensure reliable, durable performance and

compatibility with electric power system

applications

Harsh

environment

Leverage existing solutions developed for

applications in harsh environment sensing

applications to the extent possible (e.g.,

aviation, oil and gas, automotive)

Pursue foundational research in new

packaging and device materials capable of

performing within specific application

requirements. May include deployment

internal to grid assets or centralized thermal

generators achieved with advanced materials

science and engineering techniques

Advanced manufacturing of low-cost sensor

platforms

Advanced manufacturing techniques can be

leveraged to fabricate low-cost, scalable sensor

devices required to achieve appropriate balance

of cost and performance targets

Harsh

environment Develop novel manufacturing and fabrication

processes that enable advanced concepts,

such as embedded sensing and multi-

functional sensing and measurement devices

Needed advancements

Dramatic cost reductions for existing sensor

technologies

Many sensor technologies exist, but their

deployment and ultimately their impact are

limited by total deployed cost

End use

Develop radically lower-cost high-resolution

current/voltage sensors; PMU technology;

dynamic line ratings; and asset health

monitoring sensors, such as DGA and others.

Scalable weather monitoring sensors

Technologies that are customer-integrated, low-

cost, and scalable are required, especially for

grid modernization futures that will include

increasing levels of behind-the-meter

photovoltaic and DER implementation

Weather Integrate innovative technologies, including

those applied in other fields such as

agriculture sensing for variable renewable

grid integration. These technologies include

arable pulsepod, reference cells, security

cameras for sky imaging, and lidar

technologies. Extensive research is needed to

enable their integration, calibration, spectral

properties characterization, and validation for

future grid applications

30



New low-cost, multifunctional and flexible

sensor platforms

There is a lack of ubiquitous multifunctional and

flexible sensor platform technologies with

attributes specifically compatible with electric

power system monitoring applications. These

technologies can offer advantages in terms of

compatibility with standardization of data and

communication protocols and efficient

leveraging of R&D investment

End use

Develop multicomponent integrated, low-

cost sensor platform technologies for a range

of applications, including building efficiency

and power system asset health monitoring.

Platform technologies may include wireless,

self-powered, self-calibrating sensors for

large-scale deployment with capability for

auto self-configuration and commissioning.

Platforms may also include optical-based

technologies that overcome the limitations of

deploying electrical sensors in electric power

systems and within electrical assets

Sensing platforms with optimal spatial

characteristics

Localized signatures of failures or faults are

difficult to detect with individual sensors and

must instead be solved through sensor networks

Harsh

environment

Asset health

Develop complementary techniques and

platform technologies that enable multipoint

measurements, areal imaging, or linear

mapping of parameters of interest with

optimal trade-offs in spatial resolution, cost,

and performance

Passive or energy harvesting-based sensor

technologies

Energy harvesting or passive sensor

technologies can enable ubiquitous deployment

without the need for a separate power source or

connection to the electric feeder. Also, it

eliminates the need for maintenance of a battery

source

Harsh

environment Develop novel approaches to satisfy sensor

power requirements, including passive sensor

technology platforms and reliable, robust

low-cost energy harvesting techniques

Leverage Existing Ubiquitous Networks

Data from existing mobile/cellular phones could

be a source of data.

Crosscutting Crowd sourced data may be collected via

phone apps or other voluntary data collection

approaches. Phones already track certain

weather conditions.


Several capability gaps could be clearly linked to (1) optimized spectrum utilization and ease of

integration of new technology platforms into various communications networks, (2) overall architecture

characteristics, and (3) standards and protocols for communication and networking technology. Therefore,

these gaps are grouped in Table 4 within subcategories as Utilization and integration, Architecture, and

Standards and protocols.

8.2.1 Utilization and Integration

One major capability gap involves the need for optimized spectrum utilization. There is a need to address

challenges related to congestion and under-utilization within the communication infrastructure as more

numerous and varied sensing and measurement devices are deployed throughout the electric power

system. Opportunities to address this capability gap include hierarchical networks with distributed

intelligence and distributed communications scheduling schemes. Another gap identified is that advanced

communication protocols such as 5G cellular and OpenFMB are not yet fully integrated within utility

communication network architectures. Regular engagement with utilities, standards, and regulations is

31

likely to be the primary method of addressing this capability gap. A need for a larger selection of IoT

technologies capable of high (>99%) reliability and low (1 ms) latency was also identified to support the

needs of the GMI. Addressing this gap may also require the development of sensor technologies with

onboard data assimilation, analytics, and communication and with distributed intelligence to reduce the

requirement for information flow and alleviate burdens on communication systems. Cybersecurity,

particularly as it relates to data sharing, has also been identified as a significant capability gap: multiple

users across a network can cause significant challenges regarding intertwined communications and the

potential for breaches of data security and privacy. Among other potential solutions, a proposed approach

to address this gap is to develop a strict, clear framework for cybersecurity and privacy implications and

rules for the broad variety of data and data uses to assist in structuring further sensor, communications,

and architecture development.

8.2.2 Architecture

A clear capability gap was also identified for communication architectures that require compatibility with

advanced security, authentication, and communication protocols, as well as flexibility, dynamism, and

scalability. Potential approaches identified to address this gap include compiling both latency and

throughput requirements for existing and key emerging sensor platforms for which R&D is currently

being performed, and developing a compendium of IoT and Industrial IoT (IIoT) vendor and industrial

group recommended architectures. Spectrum utilization, distributed intelligence and dynamic

communication resource allocation are potential opportunities that can be achieved, for example, through

the integration of smart connectivity managers within a network architecture.

8.2.3 Standards and Protocols

Devices that are not interoperable can create interference and increase the costs and challenges of

developing and implementing new sensor technologies into the electric power system. Therefore, a

significant capability gap related to communication and networks is the need to improve interoperability

as well as standards for new and existing device communication. Potential approaches to address this gap

include (1) developing and applying improved techniques for predicting the interference and utilization

impacts of devices that are not fully interoperable, (2) developing device solutions that are agnostic to

communication technology, and (3) seeking solutions that can help ensure new devices are fully

interoperable and compatible with existing standards.

32

Table 4. Gap analysis summary for communication and networks.

Gaps identified by working groups Working groups Potential approaches to address

the gaps

Utilization and integration

Optimal spectrum utilization

Optimal spectrum utilization to

address challenges associated with

congestion and under-utilization

within the communications

infrastructure, and to optimize

scheduling of device communication

Distributed communication

Communication technologies

Engage with a variety of industry

organizations and government

agencies to understand ongoing

activities and challenges in the

communication area for leveraging

in the electric power area

Improve spectrum sharing through

techniques such as distributed

scheduling schemes for device

communication, which may include

leveraging distributed intelligence as

well

5G cellular integration for grid sensors

5G cellular services are not fully

integrated within utility communication

network architectures

Distributed

communication

Engage with utilities to determine plans for

wireless sensors and advanced cyber-physical

network topologies relevant for applicability to

electric power systems

Cybersecurity and data sharing

Multiple data users on a shared transport

medium can create challenges in terms of

intertwined communication performance

and cybersecurity across the different

layers of the network topologies

Distributed

communication

Communication

technologies

Weather

Reexamine best practice guides for control systems

applicable to electric power systems and engage

with relevant organizations and other agencies

Attempt to quantify uncertainties and security risks

associated with existing data sharing methods and

investigate techniques for dynamic routing through

“smart connectivity managers” to minimize them

Develop a strict, clear framework for cybersecurity

and privacy implications and rules for the broad

variety of data and data uses to assist in the

structuring of further sensor, communication, and

architecture development

Ensuring low latency with high

reliability

Not many IoT technologies can support

1ms latency with >99% reliability. These

requirements must also be evaluated with

respect to grid modernization use cases

Communication

technologies

Weather

Investigate both 5G- and IoT-related techniques to

clarify latency and other performance gaps as they

relate to secure electric power system

communications

Investigate distributed intelligence to reduce

information flow requirements

Develop sensors with onboard data assimilation,

analytics, and communication for a low-latency

distributed architecture that can facilitate local and

speedy control decisions

33

Table 4. Gap analysis summary for communications and networks (continued).


Lack of full leveraging of openFMB or

other advanced communications

protocols for grid sensors

OpenFMB is not fully leveraged for

electric power system/grid sensors, and

the choice of networking technology is

not necessarily obvious with many

options available

Communication

technologies

Communication

technologies

Set up a cluster of use sensor cases to determine

requirements and whether a smart connectivity

manager as a subset of an OpenFMB interface

layer or other advanced communication protocols

should be considered

Architecture

Flexible, dynamic, scalable, and

compatible architectures

Current architectures are inadequate for

advanced security and authentication

protocols (e.g., OpenFMB, ICCP V2)

A broad range of varying IoT/Industrial

IoT devices, sensors, and systems exist

and must be interfaced for deployment

throughout utility networks (including

residences). Further, with frequent

updates of sensing resources (e.g.,

GOES-R satellites and their

advancements), communication and data

assimilation architectures must be robust

to adapt and integrate with utilities

Distributed

communication

Communication

technologies

Weather

Identify throughput and latency requirements for

emerging sensor platforms (as opposed to

individual specific sensors)

Develop compendium of IoT/Industrial IoT vendor

and industrial group recommended architectures

and explore a smart connectivity manager. Enable

dynamic resource allocation and network control

features in real time, as well as plug and play

features at the device level. Incorporate distributed

intelligence into the network and seek to achieve

communication technology independent solutions

Standards and protocols

Interoperability and standards for

device communication

Non-interoperable devices create

interference and increase the costs and

challenges of developing and

implementing new device technologies

Communication

technologies

Seek ways to increase or ensure device

interoperability

Develop improved techniques to predict

interference and utilization impacts of non-

interoperable devices (e.g., machine learning at

device level)

Find or develop device solutions agnostic to the

communication technology


8.3.1 Data Management

A major capability gap related to the area of data management revolves around the standardization of data

acquisition and the need to reduce the siloing of data types/formats within specific applications. There is a

lack of established best practices and standards regarding managing, interfacing, and sharing large and

separate data sets. One potential approach to be considered involves establishing a consortium, potentially

34

within the GMLC, specifically focused on the development and application of standards for data

acquisition, distribution, sharing, and exchange that are subject to both cybersecurity and privacy

considerations within the sensing and measurement domain. Additional capability gaps identified include

a need to develop clear data requirements for accuracy, quality, and reliability and to develop or apply

methods for real-time monitoring of data quality. Potential approaches to address these gaps include the

development of clarified metrics to address the impact of data quality on various algorithms and analytics

methods for use within the modern electric power system application domain. Relevant techniques, such

as artificial intelligence and big data analytics that have been targeted specifically toward application

within PMUs through the North American Synchrophasor Initiative (NASPI), may also be adapted and

applied to the broader array of data types relevant for sensing and measurement under the GMI.

8.3.2 Data Analytics

A major capability gap in data analytics is related to the spatial aspects of sensing and measurement.

Localized events that must be detected, monitored, or quantified via advanced analytics, using only a

limited set of sensor nodes, are unlikely to be co-located at the source of the event in question. Hence,

there is a need to develop and implement techniques such as geospatial analytical methods and

incorporate disparate data sources from distinct locations and sensing platforms. Addressing data

standardization capability gaps (discussed in Section 7) can help in integrating data across multiple types

of sensor platforms and significantly improve the potential for developing and applying advanced

analytical methods. For early or incipient fault detection and rapid detection of low-probability, high-

consequence events, advanced analytical techniques can be developed and deployed in conjunction with

ubiquitous electrical parameter measurements from a wide range of data sources. In many cases,

advanced data analytical techniques can even be applied to the existing sensing and measurement

network. Data analytics also can be applied to weather and other environmental sensor and measurement

devices to meet a need for ways to accurately forecast DER generation.

Table 5. Gap analysis summary for data management, modeling, and analytics.


Data management

Standardization of data acquisition

in grid sensors

A lack of consensus and

standardization exists related to data

and communication protocols because

data formats and data collection are

not interoperable. This is particularly a

challenge for the deployment of

sensors outside of the substation,

because the integration with existing

utility tools such as a data management

system (DMS) or supervisory control

and data acquisition (SCADA) can be

a barrier. This is also true for weather

sensing information that gets ingested

by different utilities in different

formats

Harsh

environment

Data

management

Novel

transducers

Weather

Establish best practice guidelines and testing measures

for DMSs. Establish a GMLC consortium focused

around development and use of data standards.

35

Table 5. Gap analysis summary for data management, modeling, and analytics (continued).


Data availability, interfaces and

utilization

Sensing and measurement data are

disparate and owned by many different

organizations. Data are frequently

siloed (e.g., in terms of formatting)

within specific applications and not

accessible by planning entities or a

broad range of analytic tools that could

make use of them

Data

management

Weather

Establish best data interchange practices along with

tools and technologies for managing and interfacing

large disparate data sets

Establish standards and technologies for appropriately

distributing, exchanging, and sharing the data subject to

security and privacy considerations

Establish a consortium that includes data owners, users

and the communication community for cyber-secure

sharing and dissemination of data for various use cases

Unclear data requirements

(accuracy, quality, and reliability)

There is a lack of clarity regarding data

requirements for various grid

applications and analytics approaches

to accomplish system-level objectives

Weather

Develop hybrid (physics-based and data-driven) models

that relate grid applications and parameters of interest

(e.g., weather-dependent parameter forecasts, state

estimates) to understand the impacts of different

resources, varying reliability, data coverage, and

sensing infrastructure costs on application

performance—including convergent infrastructures

(energy, fuel, gas, water and transportation)

Need for data quality monitoring in

real time

Data quality from new and existing

sensors drives application performance

and algorithm usefulness. It is critical

to ensure the quality of application

results and thus of data. Industry often

considers this issue “solved” but it

often returns as a critical matter after

deployment

Data analytics

Weather

Develop consistent metrics and methodology to

evaluate the impact of data quality on a range of

algorithms across the grid and analytics domains

Develop techniques/technology to ensure data quality

for commissioning and over the operational lifetime

Explore application techniques previously developed

under NASPI, including artificial intelligence and big

data analytics for PMU data

Data analytics

Lack of leveraging data across

sensor platforms and data types

Data-driven analysis is siloed by

sensor and data type; thus the analysis

does not leverage the full range of data

available for maximal efficiency and

lowest cost

Data analytics Present use cases in a multisensor and data domain and

develop demonstrations of multimodal, multivariate

machine learning techniques for real time and

predictive analysis of a wide range of grid conditions as

presented in the use cases

36



Analytics of electrical parameters

using existing devices

Electrical parameters provide the most

rapid signatures of low-probability,

high-consequence events, such as

human or natural threats (e.g.,

geomagnetic disturbance,

electromagnetic pulse)

Data analytics Deploy analytics with existing and emerging electrical

parameter measurements to extract new value from

existing sensing and measurement devices while fully

leveraging emerging technologies

Lack of forecasting models for DER

generation

Innovative forecasting models that not

only forecast power from utility-scale

renewable resources but also from

behind-the-meter technologies are

important. In many distribution

feeders, not all of the nodes have

advanced metering infrastructure, and

utilities have little visibility of DERs

that affect the net load at the substation

feed

Weather

Use big data analytics in conjunction with numerical

weather prediction to develop probabilistic forecast

models to develop models for behind-the-meter DER

resources.

Use sky cameras and image processing to characterize

cloud impacts on solar resources and forecasted power

from renewable resources

Validate satellite data based on ground-mounted sensors

and improve the spatial and temporal resolutions of

forecasting models

Challenges of fault location with

distributed sensor networks

Non-localized signatures of failures or

faults are difficult to detect with

individual sensors

Data analytics

Develop analytics that use disparate data sources for

fault location and identification

Data-driven weather modeling

Advanced forecasting models and

their integration

The continued growth in renewable

energy, especially behind-the-meter,

necessitates innovative forecasting

models that have high spatial and

temporal resolution and that not only

forecast the mean power but also their

ramps and associated uncertainties.

The impact of variable renewables on

feeder or substation net load forecasts

must be determined as well

Weather

Data analytics

Develop advanced forecasting models for probabilistic

forecasts of load, variable renewables, and net-load

power and ramps

Use big data analytics as a tool for building data-driven

forecasting models in addition to typical weather-

forecasting models based on Numerical Weather

Prediction

Work with industry (independent system operators and

utilities) to evaluate the value proposition of advanced

forecasts and recommend best practices of forecast

integration

37



Optimal weather sensing for

different smart grid applications

Several grid modernization

applications—such as state estimation,

fault detection and system recovery,

topology estimation and feeder

reconfiguration, and stability

assessments—benefit from timely,

reliable, and accurate weather

monitoring and forecasts. Their use

can go beyond the power grid to

operations and state estimation in

interdependent systems such as

transportation, gas, and water

infrastructure. The challenge is to

understand the requirements of

weather data accuracy, quality, and

reliability for these applications and

develop cost-optimized systems for

maximum observability and grid

performance

Weather

Data analytics

Crosscutting

Develop hybrid (physics-based and data-driven) models

that relate grid applications and weather-dependent

parameter forecasts or state estimates

Understand the impacts of varying reliability, data

coverage, and sensing infrastructure cost on application

performance

Study the impact of the Pareto front of sensing

infrastructure cost and reliability on grid performance

Develop and enforce industry best practices for weather

monitoring sensor deployment, maintenance, and

operation (especially for remote locations)

38

9. CROSSCUTTING ISSUES

A number of capability gaps were identified as crosscutting. They could be categorized according to

challenges associated with (1) cyber-physical security of the sensing and measurement system;

(2) standardization of testing methods and ensuring that standards are continually being updated to reflect

the state of the art in new technology deployment; (3) establishing tools and methods to more clearly

demonstrate the value of advanced sensing and measurement technologies, data management systems,

and data analytics in the context of particular grid applications; and (4) facilitating more rapid and

widespread deployment of sensing and measurement technologies. Therefore, these gaps are grouped into

the subcategories of Cyber-Physical Security; Standards, Testing, and Standardization; Value Proposition;

and Facilitating Deployment of New Technologies.

9.1 CYBER-PHYSICAL SECURITY

The primary capability gap in cyber-physical security is the lack of focused efforts specifically targeting

cybersecurity aspects across the sensing and measurement infrastructure and for emerging specific

advanced technologies. To address this gap, the team recommended the development of clear,

standardized methodologies for assessing the cyber-physical security of emerging sensing and

measurement technologies, including awareness of cyber-physical security as a key element of new R&D

efforts focused on sensing and measurement technologies.

9.2 STANDARDS, TESTING, AND STANDARDIZATION

A key capability gap identified was insufficient standards for addressing rapidly emerging concerns

regarding the interoperability and resiliency of grid sensors. One example is the lack of a clear definition

and standardized requirement of sensor resiliency in terms of qualified standard testing procedures and

facilities. Another gap is the discrepancies in existing standards, which cause confusion in compliance

when sensing and measurement devices are developed, tested, and deployed. In some cases, as a result,

the relevant existing standards can be difficult to identify. In addition, the mechanisms for including

emerging sensor technology platforms within new standards are less than ideal. Potential approaches to

address these gaps include the development of a formalized partnership between the GMI/GMLC and

relevant standards development organizations to enable collaborative interactions. Such a partnership

could ensure that the needs of sensing and measurement technology within the electric grid application

domain are being properly addressed. The team has also noted that when relevant standards are unclear or

lacking in terms of data, communication, interoperability, or other factors, the deployment of advanced

sensing and measurement technologies can be impacted.

9.3 VALUE PROPOSITION

Another key capability gap related to the ubiquitous deployment of advanced sensing and measurement

technology is the challenges associated with providing a clear valuation of the advantages that can be

derived from the deployment of a specific technology or even a full sensor network solution. This gap is

relevant for all aspects of an advanced sensing and measurement application, including devices,

communication, analytical methods, advanced data management approaches and techniques, reliability

and resiliency, maintenance and support, and regulatory impacts. Great challenges lie in determining

accurate cost estimates for new or even existing technologies, as they may not be readily accessible and

may be different for retrofits compared with new installations. Clarifying the valuation of sensor

reliability, though difficult, could have a significant impact on the ability to clearly demonstrate the value

of advanced technology deployment. It may be addressed in part through standardized testing approaches

and through improving the understanding of full sensing and measurement costs via industry surveys and

a database with cost information, including the details of installation, operation, and maintenance.

39

Another approach is to develop standardized, well-accepted methods for the valuation of innovative

technologies. This approach might include grid modeling in conjunction with sensor placement and

allocation tools applied to high-value use cases for which different sensing and measurement technologies

and approaches can be compared on an equal basis. Such methods/tools could address sensor reliability

and resiliency in addition to performance and cost to provide a convincing relative valuation for

benchmarking. Reliability or resiliency may be difficult to estimate and quantitatively represent, but it is

critically important for actual technology deployment. SPOT, developed under the parallel task20 of this

project, is an example of such tools. It is an application-based tool to optimize the placement (number and

location) of sensors subject to application-specific objectives and constraints of physical placement and

cost/budget. Current applications completed within this tool include distribution state estimation and

system reconfiguration.

9.4 FACILITATING DEPLOYMENT OF NEW TECHNOLOGIES

Several key capability gaps were identified related to the specific need to address nontechnical challenges

to the deployment of new technologies by industry. One major gap is the need to provide industry with a

voice so that challenges, concerns, and questions regarding the deployment of advanced technologies

being developed can be heard and recognized and shared. NASPI has provided such a venue for the

synchrophasor technology community. A formalized group could be established for the broader array of

sensing and measurement technology in the future through the GMI/GMLC. For example, industry

partners participating in the working groups pointed out that, in some cases, regulations that promote the

replacement of larger capital grid assets may adversely impact the deployment of commercial and

advanced sensing and measurement technologies. A venue for voicing such concerns, lessons learned, and

other business challenges for new technology development and deployment can help better inform

regulatory bodies regarding the relevant trade-offs, and thus have a significant impact. Another gap is the

education and training associated with the deployment of new technologies. It is recognized that industry

may not always have (1) full awareness of and (2) the human capital required to train personnel to

efficiently use new systems. These gaps eventually diminish the overall value of the deployment.

Approaches to addressing these gaps may include developing new curricula specifically focused on such

topics, combined with collaboration between researchers and operators/industry focused on simplifying

user interfaces for emerging technology platforms.

Table 6. Gap analysis summary for crosscutting issues.

Gaps identified by working groups Working

groups Potential approaches to address the gaps

Cyber-physical

Lack of focused research targeting

cyber-physical security aspects

There is a lack of comprehensive research

dedicated to cyber-physical issues of

sensing and measurement systems, partially

resulting from a lack of clear awareness of

the level of cyber-physical security of these

systems, and resulting impact assessments

Crosscut

Develop clear, standardized methodologies for assessing

the cyber-physical security of emerging sensing and

measurement technologies, including awareness of

cyber-physical security as key elements of new R&D

efforts focused on these technologies

20 Task 3 of Project 1.2.5 GMLC Sensing and Measurement Strategy Project

40

Table 6. Gap analysis summary for crosscutting issues (continued).



Standards and testing and standardization

Testing standards are insufficient for

grid sensors

There is a lack of standardized testing

procedures and there are discrepancies in

existing testing standards

A clear definition of sensor resiliency and

resiliency testing requirements are lacking

Crosscut

Work with standard development organizations to

develop standard procedures and definitions

Accommodating new sensor technologies

in existing and updated standards

There are significant challenges to

identifying existing applicable standards

and interoperability requirements for

emerging sensor technologies

There are insufficient mechanisms to

accommodate emerging sensor

technologies in the development of new

standards and/or updates or standards

revisions

Crosscut

Leverage the ongoing effort by the GMLC

Interoperability project to develop a roadmap for

interoperability

Work with standard development organizations to

develop mechanisms to accommodate the incorporation

of new sensors into new or revised standards

Lack of standardization inhibits new

sensor deployment

A lack of standardized data and

communication protocols for new sensors

inhibits new deployment, particularly for

technologies installed outside the

substation, to the point that interfacing with

utility systems such as distribution

management systems, energy management

systems, and SCADAs is not trivial

Asset health

Weather

Clarify and highlight the challenge of using new

nonstandardized sensor protocols and data as a barrier to

new technology deployment and implementation

Work with utilities, independent system operators and

distribution system operators to understand format

variations and rationale, as well as develop frameworks

for standardization

Develop effective interfaces and protocols to link new

sensors and data to existing tools and data used for

power system monitoring and management

41




Value proposition

Lack of valuation for sensing and

measurement technology including data

management and analytics

There is a lack of comprehensive

capabilities and sophisticated tools to

conduct valid technology valuation and

regulatory analysis for emerging sensor,

analytics, and communication technologies

There are no standard reliable and

defensible ways to evaluate the value of

data management systems to justify their

initial and ongoing costs

Crosscut

Data

management

Develop and define well-justified and standardized

ways to express and calculate the value of improved

sensing and measurement for the power system. Apply

these in high-value use cases to develop models to

calculate the value of sensing and measurement

technology including sensors, communications, data

management, and data analytics

Pursue targeted deployment of new sensing and

measurement technologies for high-value use cases to

improve and validate technology valuation based upon

developed tools and methods

Reliability metrics need to be justified

and continually revisited

Reliability metrics play a key role in

proving value for new sensing and

measurement technologies and must be

defined and continually assessed as grid

applications and technologies evolve

PMU

Develop cases and justifications for specified

reliability metrics and define new metrics when they

are absent. This approach must be considered with

respect to current IEEE, NERC, and other standards

Existing sensors are plentiful but

expensive, limiting deployment and thus

visibility

Many grid asset monitoring technologies

exist, spanning transmission, asset

monitoring, distribution, and end use; but

their deployment, and ultimately visibility,

is limited by integration, operability, and

cost issues

Asset health

Data analytics

Novel

transducers

End use

monitoring

Develop and maintain multi-tier cost and performance

metrics to balance integration and performance versus

cost trade-offs. The goal is to dramatically reduce the

cost of existing performance and enable new lower-

cost sensors with reduced but sufficient performance

Develop analytics to leverage new and existing data

sources among different sensors and technologies

efficiently

Cost of existing technologies is difficult

to collate and assess

Cost data for existing sensors are

incomplete or need to be updated—

particularly those for operations and

maintenance (O&M. Cost metrics for new

sensing technology may also require

differentiation between new installations

and retrofits. Sensor reliability also

impacts costs and should be considered

PMU

Industry surveys for various technologies should be

performed and databases should be maintained,

without attribution to specific vendors. Reliability,

installation, O&M, and communication costs should be

explicitly considered and factored into such surveys

when possible to appropriately benchmark

technologies

An industry evaluation group could also be established

to identify costs and test for reliability and

performance

42




Support for improved but pragmatic

sensor allocation

Once the taxonomy of available sensing

resources, their cost, and their reliability is

understood, the challenge is to develop

cost-optimized systems for maximum

observability and grid performance for

various applications, subject to the reality

of practical placement constraints

Weather

Study the impacts of sensing infrastructure cost and

reliability on grid performance, including performance

during severe events

Develop flexible, broadly applicable algorithms to

maximize the cost/benefit trade-offs for practical sensor

network platforms considering the reality of constraints

(e,g., budget, accessibility, safety, location) on sensor

placement and installations

Facilitating deployment of new technologies

Giving industry partners a voice

Industry and utility partners should have a

venue for communicating and voicing

challenges that they experience related to

deploying new sensing and measurement

systems

Crosscut

The industry could set up a user group that involves

utilities and industry manufacturers so that they can

share lessons learned and other information to improve

sensors and their use and deployment. For example,

NASPI has provided such a venue for synchrophasor

technology for a number of years

Regulations inhibiting deployment of

sensing and measurement technologies

Regulations, such as ones that promote

replacement of large existing capital grid

assets, can unintentionally adversely impact

the deployment of existing and emerging

sensing and measurement technologies to

enable greater utilization of existing grid

assets

Asset health

Provide a forum for discussing business model

challenges for new sensor deployment by industry

Develop materials that can inform regulating bodies

about trade-offs between large capital replacements vs.

additional sensing and measurement technologies to

extract more value from existing assets

Coordination to fully leverage existing

sensing and measurement resources

Many existing sensing and measurement

technologies are deployed ubiquitously

throughout the electric power system on

assets not controlled by the utility and

without coordination with utility assets

Weather

Harness existing resources by providing venues for

collaboration and information exchange, such as

targeted consortia including key personnel responsible

for data generation, communication, assimilation, and

end use

Facilitate public and private data partnerships, as well as

compiled comprehensive documentation of disparate

data resources by key measurement parameters

43

10. CROSSCUTTING SENSING AND MEASUREMENT SUPPORT

As discussed in Section 9, a clear need exists for foundational efforts to support the successful technology

development and deployment of advanced sensing and measurement tools and methodologies throughout

the electrical grid infrastructure. Therefore, the team recommends that a Crosscutting Sensing and

Measurement support effort be established that spans the various research thrusts and initiatives.

The objective of this crosscutting effort is to raise awareness of identified issues that are common across

different sensing and measurement areas, create a gateway for stakeholders to efficiently access the right

expertise and resources to address the issues, and provide the support, technical or nontechnical,

necessary to facilitate those efforts.

Based on the crosscutting issues and needs identified in the working group process, four crosscutting

initiatives are recommended:

1. Cyber-physical Security Awareness and Support

2. Standards and Testing to Support Improvement of Sensor Performance, Reliability, Resiliency, and

Interoperability

3. Valuation of Sensing and Measurement Technology

4. General Crosscutting Needs Support for Industry and Utility Partners in Technology Deployment

Initiatives 1–3 would focus on technical issues common across all types of sensing and measurement

technologies covered in the report. Initiative 4 would be a long-standing venue to support industry and

utility partners with general crosscutting needs, even after the activities of the other initiatives have been

closed. The approaches for these initiatives can be summarized as reviewing and documenting existing

knowledge; harmonizing existing requirements and standards; developing new definitions, standards, and

tools/methods; and providing guidance and support. Some of the proposed development and analysis

work can possibly be developed into future stand-alone projects (under GMLC or other funding support).

Some can be related to or tied in with existing GMLC projects, the results and findings of which can be

readily used to address the crosscutting issues. It is also possible for some of the proposed crosscutting

activities to be merged or coordinated with existing efforts.

10.1 CYBER-PHYSICAL SECURITY AWARENESS AND SUPPORT

Sensing and measurement systems in the power grid are on the front lines of susceptibility to cyber-

physical threats. However, awareness of the cyber-physical security issues of the sensing and

measurement systems, in some sense, remains at a qualitative level, lacking in-depth understanding of

challenges and technical details that are specific to sensing and measurement devices. The great diversity

of sensors used in the power grid makes it more difficult to address these issues. Some sensors may have

built-in cyber-physical security features. However, many sensors operating in the power grid contain

numerous components; this increases their susceptibility and requires more sophisticated cyber-physical

solutions. Therefore, room exists for top-down and comprehensive research on regarding the cyber-

physical security of the power grid’s sensing and measurement systems.

The primary capability gap within the area of cyber-physical security is the lack of focused efforts

specifically targeting cybersecurity across the sensing and measurement infrastructure, as well as for

specific advanced technologies that are emerging. To address this gap, the team recommends

development of clear, standardized methodologies for assessing the cyber-physical security of emerging

44

sensing and measurement technologies, including awareness of cyber-physical security as a key element

of new R&D efforts focused on sensing and measurement technologies.

This crosscutting initiative is to raise awareness of the cyber-physical security concerns of the sensor and

measurement systems in the power grid by developing more technically oriented guidance and reference.

The security challenges and gaps in the existing sensor infrastructure will be analyzed. Comprehensive

cyber-physical requirements for sensor systems used in power grid applications will be summarized and

documented. The initiative will also provide support to stakeholders (mostly the corresponding

researchers, sensing technology developers/vendors, and sensor system users) in improving the security of

existing sensor and measurement infrastructure and developing new sensor projects with built-in

reinforcement of cyber-physical security. It will facilitate the communication channels needed to bring the

right expertise and resources to stakeholders to address the cyber-physical vulnerabilities regarding

sensing- and measurement-based applications in the power grid. An existing GMLC project related to

cybersecurity is as follows:

GMLC Project 1.4.23, Threat Detection and Response with Data Analytics, is to develop advanced

analytics on operational cyber data to detect complex cyber threats in the power grid. This project will

help power operators differentiate between cyber-caused and non-cyber-caused incidents—for example,

physical attacks or natural hazards. It may also provide a tool to support the cyber-physical security needs

discussed in this crosscutting initiative.

10.2 STANDARDS AND TESTING TO SUPPORT IMPROVEMENT OF SENSOR

PERFORMANCE, RELIABILITY, RESILIENCY, AND INTEROPERABILITY

While concerns regarding interoperability and resiliency rapidly grow in the context of making the power

grid more flexible and resilient, great insufficiency remains in the existing standards and testing

procedures for grid sensor interoperability and resiliency. There is a lack of clear definition and

standardized requirements for sensor resiliency in the form of qualified standards, testing procedures, and

facilities. Unique DOE laboratory facilities can potentially help to support the testing and standard

development efforts in this area, in collaboration with other organizations.

The types of sensors used in the power grid and their communication setups vary significantly based on

their application. This variability results in complications in identifying the appropriate standards and

interoperability requirements with which sensing and measurement technologies and their deployment

should be compliant. This is especially true for emerging technologies and advanced sensors. There are

discrepancies in existing testing standards, causing confusion regarding compliance when the sensing and

measurement device is developed, tested, and deployed. On the other hand, the development of new

standards and interoperability requirements should account for emerging technologies and trends.

Unfortunately, the mechanisms for including emerging sensor technology platforms within the process for

developing new standards are less than ideal.

This crosscutting initiative will target the establishment of standardized definitions, methodologies, and

procedures for the benchmarking and testing of the functional performance, reliability, and resiliency (in

the presence of extreme natural or human-caused events) of sensors before full deployment occurs. Also

included is the development of a formalized partnership between the GMI/GMLC and relevant standards

development organizations. Coordination between the two organizations will enable collaborative

interactions that can ensure that the needs for sensing and measurement technology within the electric

grid application domain are properly addressed. Also, existing standards can be harmonized to eliminate

discrepancies. These activities will also promote the establishment of a database of testing facilities with

comprehensive capabilities in performance and reliability testing, as well as intrusive testing to validate

45

sensor resiliency. Finally, strategic partnerships with private and public-sector partners will establish

access to relevant testing facilities.

Within GMLC, several ongoing projects related to this initiative have been identified:

• GMLC Project 1.2.2 Interoperability—The objective is to articulate general interoperability

requirements along with methodologies and tools for simplifying the integration and cyber-secure

interactions among various devices and systems. It will involve establishing a strategic vision for

interoperability, measuring the state of interoperability in technical domains, identifying gaps and

roadmaps, and ensuring industry engagement.

• GMLC Project 1.4.1 Standards and Test Procedures for Interconnection and Interoperability—The

objective is to help develop and validate interconnection and interoperability standards for existing

and new electrical generation, storage, and loads. The activity will ensure cross-technology

compatibility and harmonization of jurisdictional requirements, and ultimately will enable high

deployment levels without compromising grid reliably, safety, or security.

• GMLC Project SI-1695 Accelerating Systems Integration Codes and Standards—The objective is to

update the standards identified under the grid performance and reliability topic area, focusing on the

distribution grid. Also, this project will establish accelerated development of new interconnection and

interoperability requirements and conformance procedures, which is the key project result.

• GMLC Project 1.2.3 Grid Modernization Laboratory Consortium Testing Network—The objective is

to close the gap in accessibility to validated models for grid devices and simulation tools and

corresponding full documentation. The project will drive the standardization and adoption of best

practices related to device characterization, model validation, and simulation capabilities through

facilitated industry engagement. Some of the project’s findings may help address the testing issues

brought up in this crosscutting initiative.

10.3 VALUATION OF SENSING AND MEASUREMENT TECHNOLOGY

Clear valuation is among the defining factors by which utilities make decisions on adopting advanced

sensing and measurement technologies. Valuation is relevant for all aspects of a sensor application,

including devices, communication, analytical methods, advanced data management approaches and

techniques, reliability and resiliency, deployment, maintenance and support, and regulatory impacts.

Successful valuation usually involves extensive analysis and quantitative modeling of technical and

economic risks and benefits. However, significant challenges exist in providing accurate cost estimates

for new or even existing technologies, as they may not be readily accessible, and costs may be different

for retrofit projects compared with new installations. The costs and benefits due to some factors, such as

sensor reliability and resiliency, are even more difficult to quantify; but they could have a significant

impact on the ability to clearly demonstrate the value of advanced technology deployment. In current

practice, the lack of comprehensive capabilities and sophisticated tools to conduct valid valuation is a

major barrier to promoting new technology. In addition, regulation may affect technology adoption and

deployment, making the analysis more complicated. For example, regulatory incentives can encourage the

adoption of new technologies, whereas regulatory restrictions may induce extra costs and discourage

adoption.

Standardized testing approaches, industry surveys, and a cost information database (including the details

of approval, installation, operation and maintenance, and so on) may improve understanding of total

sensing and measurement costs. The development of standardized, well-accepted methods/tools for the

valuation of technologies also is necessary. The methods/tools may integrate grid modeling with sensor

46

placement and allocation capabilities, which can facilitate comparisons among different sensing and

measurement technologies on an even basis. Such methods/tools should address sensor reliability and

resiliency in addition to performance and cost to provide a convincing relative valuation for

benchmarking, as reliability or resiliency may be difficult to estimate and represent quantitatively but are

critically important for actual technology deployment. SPOT as mentioned earlier is an example of such a

tool.

This crosscutting initiative is to support the adoption of sensing and measurement technologies by

promoting capabilities and methodologies for improved valuation. It will promote the establishment of

expertise and capabilities both internal and external to the DOE national laboratory system to facilitate

technology valuation, regulatory analysis, and risk evaluation of sensor deployment projects. Relevant

methods, tools, research efforts, and best practices will be identified and categorized with up-to-date

contact information, and the results will be made accessible for the stakeholders.

Some ongoing projects within GMLC are related to the topic of this initiative, the findings and results of

which might be worth consideration for the proposed work of this initiative. GMLC Project 1.2.4 and

1.4.29 are two examples. Project 1.2.4, Grid Services and Technologies Valuation Framework, is to

address the inconsistencies and lack of transparency across existing valuation methodologies by

developing a comprehensive and transparent framework to value the services and impacts of grid-related

technologies. The valuation framework must be useful to assess “regulated investments” as well as

investments by private sector entities. The proposed valuation framework might be used for sensing and

measurement technologies. Project 1.4.29, Future Electricity Utility Regulation, assists states in

addressing regulatory, rate-making, financial, business models, and market issues related to grid

modernization in the power sector. It will also help link utility earnings to consumer value, economic

efficiency, and other public policy goals. Some findings of the project may directly benefit this

crosscutting initiative by providing answers to issues such as how to adapt electric utility regulation and

rate-making to new technologies and services, assess potential financial impacts on utility shareholders

and customers, invest in infrastructure that enables customer engagement, and how to provide incentives

to utilities to achieve grid modernization goals.

10.4 GENERAL CROSSCUTTING NEEDS SUPPORT FOR INDUSTRY AND UTILITY

PARTNERS IN TECHNOLOGY DEPLOYMENT

Beyond the aforementioned initiatives and activities, there is a need for long-term and continuous efforts

to support the industry and utility partners in some general crosscutting issues. Examples may include

continuous maintaining and updating of contact information, expertise lists, and technology databases,

and providing support for recurring events (e.g., industry meetings/workshops). Also, some new

crosscutting needs, such as expertise matchmaking, may arise on a project-by-project basis. Therefore,

having a standing mechanism, which is missing in the current setup, to support those needs is necessary

and can be beneficial in the long run. Such an initiative is recommended.

Several key capability gaps were identified related to the specific need to address nontechnical challenges

concerning the deployment of new technologies by industry. One major gap is the need to provide

industry with a voice so that challenges, concerns, and questions regarding the deployment of advanced

technologies being developed can be heard and recognized. For example, industry partners pointed out

that in some cases regulations that promote the replacement of large capital grid assets can adversely

impact the deployment of existing and advanced sensing and measurement technologies. A venue for

voicing such concerns and other business challenges for new technology development and deployment

can inform regulatory bodies regarding relevant trade-offs and thus have a significant impact. Another

gap is the education and training associated with the deployment of new technologies.

47

This crosscutting initiative is to provide a long-standing mechanism to support industry and utility

partners in general crosscutting needs induced by sensor deployment. As mentioned earlier, a formalized

group could be established for the broader array of sensing and measurement technologies in the future

through the GMI/GMLC. This initiative will promote the establishment of relationships and partnerships

among the research, academia, industry, utility, and regulation communities. It is expected to provide a

standing venue for stakeholders to voice the challenges they face in developing and deploying new

sensing and measurement technologies within their systems. The establishment of two-way

communication between regulation-makers and stakeholders would help resolve misunderstanding and

inconsistency to accelerate technology adoption and deployment. Regular workshops with industry and

utility partners would maintain a working knowledge of barriers preventing new sensing and

measurement technology deployment. At these meetings, lessons learned and needs for new expertise and

facilities could be communicated with DOE and GMLC leadership to identify opportunities where

resources within the DOE system can be leveraged to provide assistance.

48

11. HIGH-VALUE USE CASES AND THE EXTENDED GRID STATE DEFINITION

The EGS definition derives from the concept that the state of the grid consists of more than a set of

electrical measurements (i.e., those of a state estimator, for those familiar with this technology). The EGS

includes traditional electrical aspects as well as markets, communications, utility asset states, and ambient

conditions such as weather and other environmental factors. The EGS provides a holistic basis on which

to map scenarios and use cases to examine utility sensing and measurement to enhance grid reliability and

resiliency and provide direction for future R&D. A set of eight use cases was developed by the team for

focusing on, exploring, and demonstrating the need for a sensing and measurement strategy relevant to

the roadmap development.

A complete description of these use cases is presented in Appendix E. They include

1. Fault Detection, Interruption, and System Restoration

2. Incipient Failure Detection in Electrical Grid Assets

3. Sensing and Measurement Technology to Mitigate or Prevent Impacts of Cyber or Manmade Attacks

4. Integrating Advanced Resource Forecasts for T&D Grid Operations

5. Topology Detection within the Distribution System.

6. Sensing and Measurement Technology to Mitigate Impacts of Natural Disasters and Enhance Grid

Resilience

7. Optimizing Grid Operation with Enhanced Data Spanning Transmission, Distribution, and Generation

8. Detection of Energy Theft and Unregistered DER

Of these developed use cases, the three highest priority cases are 1, 2 and 3. They have been further

developed based on their potential impact, diverse characteristics, and differing EGS utilization.

The following sections describe each high-priority use case in terms of the latest EGS framework version.

11.1 FAULT DETECTION, INTERRUPTION AND SYSTEM RESTORATION

The detection and interruption of faults and the restoration of the power system after disruptions is key to

ensuring the reliability of distribution systems. The evolution of the smart grid with high penetration

levels of DERs makes it more challenging to maintain the high level of reliability that we have today.

Thus, it is critical that protection devices be both properly and adequately placed and their protection

settings adjusted as the state of distribution circuits varies with the changing status of DERs on the circuit.

Widespread deployment of these devices also requires advances in distributed communications

architectures and efficient data management. Fault detection, interruption, and system restoration

technologies that employ both switchgear and control logic are deployed to provide more reliable power

to distribution systems. However, there is no existing methodology for the systematic deployment of

these devices; only rule-of-thumb methods are currently used by engineers, such as the placement of two

or more of these devices on long distribution circuits. There is also no existing methodology for the

placement of this technology to take into account various levels of distributed resources in the system,

which impact protection device locations and settings. A sensor optimization placement framework and

tools for determining how fault detection, interruption, and system restoration devices—such as

49

intelligent recloser—should be developed. Doing so will achieve optimal reliability on distribution

systems both with and without distributed resources. Figure 5 shows how this use case relates to the EGS.

11.2 INCIPIENT FAILURE DETECTION IN ELECTRICAL GRID ASSETS

Early detection of incipient failures at and within electrical grid assets is a ubiquitous need throughout the

electrical grid infrastructure. For many critical assets, sensing and measurement device technologies exist.

Reducing costs, or increasing the overall valuation proposition, and improved data analytics

methodologies should be studied. Machine learning methods can improve deployment and performance,

and enable successful detection of incipient faults on the broadest possible range of grid assets. Figure 6

shows how this use case interfaces with the EGS. The general idea encapsulated in this figure is to merge

measurements taken from the operational electrical system with structural information about the

components and asset information about the history and life cycle of a component or asset, possibly

enhanced by novel sensors to directly measure the properties of their condition. The information would be

merged through analytical methodologies to enable forecasts and observations regarding probable or

imminent component/asset failures before equipment failure and to provide information to geospatial

databases. Such information could improve the performance of operations and maintenance programs and

proactively mitigate outages versus reactively responding to them after the fact.

50

Figure 5. EGS relevance to fault detection, interruption, and system restoration.21



51

Figure 6. Extended grid state relevance to incipient component failure.22



52

11.3 SENSING AND MEASUREMENT TECHNOLOGY TO MITIGATE AGAINST IMPACTS

OF NATURAL DISASTERS AND ENHANCE GRID RESILIENCE

Recent severe power outages caused by extreme weather hazards highlight the importance and urgency of

improving the resilience of the electric power grid. Improving the speed and efficiency of distribution

system restoration can play a key role in enhancing grid resiliency against natural disasters. One key

challenge for distribution system management and restoration during natural disasters is improved

situational awareness of the operational state and damage status. An increased awareness of the EGS

resulting from sensor technology and data fusion would improve operations, planning, management, and

restoration throughout the course of a major grid event. Achieving this awareness requires sensing the

ambient conditions under which a grid operates, integrating the available resources and assets, and

continually monitoring the electrical and communication states of the grid. Such a coherent understanding

of the grid in challenging conditions is possible only through detailed measurements and analytics defined

by the EGS. Figure 7 shows this use case.

11.4 SUMMARY OF USE CASES

The three high-value use cases described highlight the importance of the EGS definition in addressing

challenging grid problems. These problems cannot be adequately addressed without a holistic view of the

grid via the use of advanced sensors and data analytic techniques. In these use cases and many others, a

coherent understanding of the state of the grid improves grid reliability in ways that are not otherwise

achievable. Complete visibility of the state of the grid can be achieved only through novel

inexpensive/high-value sensing and measurement technology, reliable and secure communication,

coherent and efficient data management, and novel data analytics techniques.

53

Figure 7. Extended grid state relevance to natural disaster mitigation.23



54

12. KEY FINDINGS AND PROPOSED FEDERAL EFFORTS TO ADDRESS GAPS

Based on the identified capability gaps summarized in Section 9, and described in more detail in the

individual working group summary reports of Appendix D, the overall industry partner/stakeholder and

national lab team has developed potential initiatives and research thrusts that may require federal

investment/participation to be achieved. The working group leads were also asked to recommend relative

priorities based upon the information gathered during their respective working group processes. These

recommendations are included within the detailed working group reports in Appendix D. The proposed

initiatives and thrusts were then further prioritized according to working group and stakeholder

engagements. (The most recent one was an in-person workshop held at Southern Company in Atlanta,

Georgia, in spring of 2018.)

The key findings are summarized in this section. The full set of research thrusts, along with targets,

timelines and relative rankings and prioritization, are presented in Section 13. The key findings are

grouped into (1) Uses and Sensing Technology Targets, (2) Communications and Networks, and (3) Data

Management and Analytics and Modeling.

12.1 USES AND SENSING TECHNOLOGY TARGETS

1. Many commercial technologies exist, yet deployment is limited by the total overall cost (equipment

and installation) of implementing sensing technologies and the return on investment perceived by the

owner of the assets to be monitored. In order to enable and accelerate new sensing technologies,

federal research efforts should specifically target (a) dramatic cost reductions for equipment with

performance comparable to that of existing commercial technologies and (b) extremely low-cost

(e.g. less than between $1 to $100 per node or sensing location) sensing approaches that enable

access to parameters of interest with adequate but reduced overall performance levels.

2. Enabling technologies such as advanced sensing materials and scalable low-cost manufacturing

methods can significantly impact the performance and cost of advanced sensing devices, and are a

core capability of the DOE national laboratories. Federal research efforts should specifically

leverage DOE laboratory and other capabilities in advanced materials and advanced/additive

manufacturing methods for developing novel multi-modal and multi-parameter, low-cost sensor

platforms that meet specified cost and performance targets.

3. Generation assets, such as fossil and nuclear-based plants, impose extreme performance constraints

on asset health monitoring sensing technologies due to operational temperatures, pressures,

erosive/corrosive conditions, and potential for radiation exposure. Federal research efforts on asset

health monitoring of conventional generation assets should specifically target high-temperature

(e.g. 500 to 1500oC or higher) and harsh environmental performance operational conditions (e.g.

corrosive, erosive, and radiation) with cost as a secondary consideration.

4. Temperature is a key parameter in the early identification of faults and failures in assets across the

modern power system. Federal research efforts should target novel temperature-sensing

approaches for internal asset monitoring through emerging technologies with unique

characteristics, such as compatibility with deployment internal to both electrical grid and

generation assets.

5. Electrical parameter measurements can provide the most rapid signatures of low-probability, high-

consequence events, such as physical (i.e., human-caused) or natural events, to enable preventative

action that can prevent large-scale failures and minimize impacts to achieve grid resiliency. Abnormal

behavior (such as failures, faults, or severe degradation of performance of an asset) also often

55

manifests itself by deviation from nominal grid operating frequency or by the occurrence of abnormal

frequencies (such as previously unexperienced or undetected harmonics or frequency characteristics).

Federal research efforts should target rapid, high-bandwidth and low-latency electrical parameter

measurements, including novel frequency-selective sensors that can provide fundamentally new

information.

6. A unique value proposition exists for asset health-monitoring sensors that (1) are capable of

monitoring multiple parameters of interest simultaneously (e.g., temperature, pressure, and gas phase

chemistry), (2) are compatible with internal electrical and generation asset deployment, and (3) enable

spatially distributed measurements. Federal research efforts should target sensor technology

platforms with these unique characteristics, such as optical and passive wireless sensor device

technologies and areal imaging–based techniques.

7. Indirect measurements of proxy parameters that are relatively easy and inexpensive to implement are

often sufficient. They can take measurements external to an asset and can provide insights about asset

health and faults/failures. Federal research efforts should encourage development of ultra-low-cost

proxy-based sensing platforms (e.g. acoustic, ultrasonic, and corrosion proxy sensors at $1 per

node).

8. Wireless, self-powered, self-configuring, self-commissioning, and self-calibrating sensors for

building efficiency will be necessary for future transactive controls. Federal research efforts should

target development of low-cost, wireless, self-powered, self-calibrating, and multicomponent

integrated sensors for large-scale deployment.

9. Electricity, temperature, luminance, air quality, building occupancy, and so on are measured by

different types of equipment and are typically not correlated for advanced functions like fault

detection and diagnosis (FDD) of building equipment. Federal research efforts should encourage

development of multi-sensor integrated measurement devices that are passive or self-powered,

interactive, and intelligent for comprehensive self-learning/adaptive controls.

10. It is vital to consider the vast amount of existing weather-monitoring sensor and measurement

infrastructure, in conjunction with possible newer infrastructure, and find ways to harness them for

various types of advanced grid modeling and operational integration. Federal research efforts should

target the development of low-cost scalable weather sensors, high-quality and portable calibration

techniques, and more optimal utilization of existing weather-monitoring infrastructure for data-

driven advanced system modeling. Advanced modeling and integration needs include load

dynamics and forecasts; probabilistic renewable energy forecasts; strategic planning against

natural disasters for grid resilience; and integration of advanced forecasts into energy

management and distribution management systems operation for economics, lean reserves, and

reliability.


1. Utilities have deployed communication networks that support their present operations and in most

cases not ones that enable widespread use of sensors. Federal research efforts should target design

and development of a cost-effective, scalable communications fabric to support the wide range of

next-generation sensors, systems, and DER, electric vehicle, and responsive load components.

2. The IIoT and 5G wireless activities under way in the private, public, and academic sectors present an

array of concerns for electric utilities, including changes in the supervisory control and data

acquisition/incident command system (SCADA/ICS) architecture, cybersecurity vulnerabilities, and

56

use of the Cloud for data archiving and operations. Federal research efforts for designing a

distributed communications architecture that supports these technology developments as well as

provides cybersecurity is under way and should continue.


consequence events, such as physical (human-caused) or low-occurrence natural events, to enable

actions that can prevent large-scale failures and minimize impacts that degrade grid resiliency.

Federal research efforts should target development of scalable, rapid, high-bandwidth and low-

latency communication networks to support cybersecure transport of data associated with electrical

parameter measurements.

4. Optimal spectrum utilization remains a challenge to be addressed, as many distinct grid sensors are

deployed across the modern electric power system. In addition, flexible, scalable, and dynamic

architectures are required to support the needs of such sensor deployments. Federal research efforts

should target spectrum utilization challenges, including distributed scheduling schemes and

distributed intelligence, as well as dynamic resource allocation.

5. The uncertainties and security risks associated with networking techniques should be addressed.

Cybersecurity and data privacy should remain key factors in the development and implementation of

new technologies and networks. Federal research efforts should quantify network uncertainties and

security risks in the context of the modern electric power system and develop self-healing and more

robust network capabilities to oppose malicious operations.


1. Considerable amounts of R&D are occurring at many institutions. These include commercial,

educational, and government-sponsored R&D into data management, and various technologies for

dealing with data. Numerous technologies of various kinds were noted by the working groups.

However, few of them are making their way into power grid operations for three reasons: cost

justification, workforce education, and standardization. Federal research efforts for data

management in the utility sector should specifically focus on addressing these gaps.

2. A primary reason why more advanced data management and analytics are not being used by operators

for grid operations is that the displays and indicators are not usable or desirable in a grid control room

(because operators already have too much information output to monitor). Control room operators are

required to perform decision actions when new tools are introduced, so there is a high bar for getting

new tools/displays introduced. This situation highlights a disconnect between researchers and

operators about how humans operate in the control room environment. Federal research efforts on

data management for grid visibility should include a focus on human-machine interactions with

visualization and should engage operators early in the development process.

3. Data preparation (e.g., data format, quality) is a key limitation for data analysis and should be

considered a key gap within data analytics rather than the analytics themselves. Federal research

efforts should target efforts to standardize data formats and interfaces, as well as develop and apply

techniques for data quality monitoring and processing in real time.

4. Multimodal and multivariate analyses, integrating new sensing types and considering synchronization

and reconciliation of these data sets, would be a valuable contribution. Federal research efforts

should target development and application of data analytical methods that enable coupling of

sensors of varying types and time synchronization to accomplish the desired objectives of operating

and planning a modern electric power system.

57

58

13. PROPOSED RESEARCH THRUSTS INCLUDING METRICS

In light of the key findings and capability gaps identified in earlier sections and explained in more detail

in the appendices, a number of specific research initiatives/thrusts were developed as potential

federal/industry R&D endeavors to fill these gaps. The research initiatives—in the cases of crosscutting

needs and R&D thrusts and of devices, communications, and data analytics—include rationale, scope of

proposed activities and, where possible, identifiable quantitative metrics. Linkages with the EGS as well

as the desired attributes of a modern electric grid are also identified. A suggested timeline for the

proposed research efforts and prioritization (1–5, with 1 being the highest priority) across all focus areas

as well as a ranking within each focus area were also determined via working group activities. This

information is provided in a graphical timeline at the end of each focus area.24 Targets, timelines, and

recommendations were developed by the working group leads in close consultation with key stakeholders

from industry, utility, government, academia, and the DOE laboratories through the working group

processes identified in Appendix D. It is anticipated that these suggested R&D efforts (initiatives and

thrusts) will serve as useful input to both DOE and industry for future decisions and plans regarding

sensing and measurement technology development for grid modernization.

24 This is a modification of the approach taken in the EPRI Transmission and Substation Area Roadmap documents.

59

CROSSCUTTING INITIATIVES

The objective of this crosscutting effort is to raise

awareness of the identified issues that are in common

across different sensing and measurement areas; create a

gateway for stakeholders to efficiently access the right

expertise and resources to address the issues; and provide

support, technical or nontechnical, necessary to facilitate

those efforts.

1: Cyber-Physical Security Awareness and Support

Raise awareness of the cyber-physical security of the

sensor and measurement systems in the power grid. This

effort will also provide support to stakeholders (mostly the

corresponding researchers, sensing technology

developers/vendors, and sensor system users) in improving

the security of existing sensor and measurement

infrastructure and developing new sensor projects with

built-in reinforcement of cyber-physical security.

Scope of activity: (1) Analyze the security challenges and

gaps in existing sensor infrastructure. (2) Summarize the

cyber-physical requirements for sensor systems used in

power grid applications. (3) Facilitate the communication

channels to bring the right expertise and resources to the

stakeholders to address the cyber-physical vulnerabilities

regarding sensor and measurement applications in power

grid.

2: Standards and Testing to Support Improvement of

Sensor Performance, Reliability, Resiliency, and

Interoperability

(1) Target the establishment of standardized definitions,

methodologies, and procedures for the benchmarking and

testing of sensor functional performance, reliability and

resiliency (in the presence of extreme natural or human-

caused events) before engaging in the full deployment

phase. (2) Develop a formalized partnership between the

GMI/GMLC and relevant standards development

organizations to enable collaborative interactions that can

ensure the needs for sensing and measurement technology

within the electric grid application domain are being

properly addressed. (3) Harmonize existing standards to

eliminate discrepancies. (4) Promote the establishment of a

database of testing facilities with comprehensive

capabilities in regular performance, reliability tests, and

intrusive tests to validate resiliency. (5) Establish strategic

partnerships with private- and public-sector partners to

enable access to relevant testing facilities.

Scope of activity: (1) Define standardized definitions,

methodologies, and practices for benchmarking and testing

of sensor performance, reliability, and resiliency. (2)

Harmonize existing testing standards to eliminate

discrepancies. (3) Maintain an up-to-date understanding of

standards and testing facilities that have comprehensive

capabilities. (4) Develop strategic partnerships with

private- and public-sector partners to enable access to

relevant testing facilities. (5) Provide technical input into

new standards through active participation and

engagement. (6) Develop sensor-specific working groups

and consortiums for measurement quality assurance and

format standardization for utility integration.

3: Support for Sensing and Measurement Technology

Promotion and Deployment

(1) Support the adoption of sensing and measurement

technologies to promote the capabilities and methodologies

for improved valuation. (2) Promote the establishment of

expertise and capabilities both internal and external to the

DOE national laboratory system to facilitate technology

valuation, regulatory analysis, and risk evaluation of sensor

deployment projects. (3) Identify and categorize relevant

methods, tools, research efforts, and best practices with up-

to-date contact information, and make accessible for

stakeholders.

Scope of activity: (1) Identify and categorize relevant

capabilities, tools, research efforts, and best practices for

technology valuation with up-to-date contact information,

and make these results accessible to the stakeholders . (2)

Promote the development of methods/tools that can

integrate grid modeling with sensor placement and

allocation capabilities to address valuation of sensor

reliability and resiliency. (3) Conduct detailed value

proposition analysis that considers multiple value streams

for different stakeholders, various different sensing

technologies to ensure grid and resource visibility, and a

Pareto front of solutions that varies in costs and benefits.

(4) Analyze the impact of varying levels of sensing systems

performance on grid economics and reliability.

4: General Crosscutting Support for Industry and

Utility Partners

(1) Provide a long-standing mechanism to support industry

and utility partners in general crosscutting needs for sensor

deployment. (2) Promote the establishment of relationships

and partnerships among the research, industry, utility and

regulation communities. (3) Provide a standing venue for

stakeholders to voice the challenges they face in the

development and deployment of new sensing and

measurement technologies within their systems.

Scope of activity: (1) Hold regular workshops with

industry and utility partners to maintain a working

knowledge of barriers preventing new sensing and

measurement technology deployment. (2) Share lessons

learned and needs for new expertise and facilities with

DOE and GMLC leadership to identify opportunities where

technical resources within the DOE system can be

leveraged to provide assistance. (3) Establish two-way

communication between regulation-makers and

stakeholders to help resolve misunderstanding and

inconsistency to accelerate technology adoption and

deployment.

60

61

DEVICES

Harsh Environment Sensors—R&D Thrusts

Flexible operation of conventional power plants refers to the

potential of fossil and nuclear energy to serve applications

other than their traditional baseload operations as part of the

grid modernization strategy. In addition to baseload and

spinning reserve (which applies to gas- and coal-fired

generation), power plants can provide additional services

through flexible operation. For example, plants can follow the

variability of responsive load and renewable energy; provide

ancillary services, provide spinning and non-spinning reserve

capacity; reduce peak load; and controllably interact with

newer grid assets such as energy storage and demand response.

There is also the potential for nuclear- and fossil-based

generators to be built as smaller plants closer to the distribution

system and provide the variety of services mentioned

previously, including as baseload sources. Newer services will

require that such plants be flexibly operated over a wider range

of operating capacities and for more extreme swings

(increasing and decreasing) in power ramp rates, while

maintaining reasonable costs, reliability, emissions levels, and

energy efficiency and while not impacting component

lifetimes. Enhanced capabilities for internal monitoring of

power generation processes in real time enable advanced

control strategies and enable the development of conventional

plant designs to reduce any potentially adverse impacts on the

generators, as well as more rapid adoption of newer

technologies compatible with energy-efficient and flexible

operation.

In many cases, high temperatures and harsh environmental

conditions, which consist of highly corrosive and oxidizing gas

species, present significant challenges for conventional sensors

and instrumentation systems. In the case of nuclear power

plants, radiation hardening of emerging instrumentation

systems is an additional challenge that must be addressed.

Key measurement parameters: Temperature, pressure,

chemistry, emissions, flow rate, heat flux, flame characteristics,

mechanical performance (stress, strain, deformation, vibration,

acceleration), current, voltage, frequency, real and reactive

power, neutron and gamma flux (intensity and energy

spectrum), radiation detection (specific to nuclear systems

only).

1: Harsh Environment Sensing for Real-Time Monitoring

Harsh-environment embedded sensor technology is required for

monitoring of conventional generation processes to enable the

optimized flexible operation needed for a modern electrical

power system operation.

Key measurements: Chemistry, temperature, pressure, heat

flux, flow rate, mechanical performance

Key metrics:

Temperatures (700–1800°C), chemistry (H2, CH4, O2, CO,

CO2, sCO2, N2, NOx, SO2, volatile matter), pressures (up to

~107Pa), cost (varies), durability (component lifetime,

maintenance intervals, or specified replacement period)

Attributes: Flexibility, resiliency, sustainability

EGS level: Component state

Scope of activity: Conduct sensor device technology

development at laboratory scale, followed by pilot-scale

deployment and testing, leading ultimately to technology

transition to industry.

2: Advanced Electromagnetic Diagnostic Techniques

Develop electromagnetics-based diagnostic approaches needed

to enable real-time monitoring of generation processes, for

example, through access ports or using tomography-based

techniques.

Key measurements: Solid flow, particulate characterization,

temperature, current density

Key metrics:

Cost (varies), temperatures (700–1800°C), current density

(A/cm2), particle size (micron to mm), resistance to port

contamination

Attributes: Flexibility, resiliency, sustainability




deployment and testing, ultimately leading to technology

transition to industry.

62

63

Grid Asset Health Performance Monitoring—R&D

Thrusts

Monitoring to determine asset heath condition can be applied to

all assets within the electrical power system. Benefits derived

from improved visibility of asset condition and health include

increased reliability and resilience through prevention of

catastrophic failures of critical assets and implementation of

condition-based maintenance programs as a substitute for run-

to-failure or time-based applications. It is desirable, in the

movement toward a modern electric power system, to develop

improved sensor device technologies at sufficiently low cost to

monitor asset health and performance in greater quantity with

higher visibility. In regard to electrical parameter

measurements, please refer to Novel Electrical Parameter

Sensors—R&D thrusts. 25

Attributes: Reliability, resiliency, security

EGS level: Component state, convergent network state



deployment and testing and ultimately technology transition to

industry.

Key Assets

Large Power Transformers

Large power transformers (>~100 MVA) represent a major

critical asset class for which failures can be catastrophic and

costly. Sensor technologies currently exist; however, further

advances can enable improved identification of transformer

performance degradation—by dissolved gas analysis (DGA),

internal temperature measurements, insulation oil level

monitoring, and transformer bushing fault detection—prior to

catastrophic failure. Lower-cost approaches would also enable

more ubiquitous implementation.

Key measurements: Temperature, chemistry, moisture, oil

level, fault currents, voltage, vibration, ambient temperature,

25 Electrical parameter measurements can play a key role

in asset monitoring. But detailed R&D thrusts related to

all electrical parameter sensors are condensed in the

Novel Transducers area (Appendix D4) to best leverage

synergies with regard to electrical parameter sensing

across the entire range of applications within the power

system.

internal pressure, cumulative operating conditions (stresses

over time), tilt/sag

Distribution system equipment

Distribution-level grid assets including power transformers,

capacitor banks, switches, circuit breakers, distribution lines,

and others have not been heavily instrumented from a health

monitoring perspective because of the high cost and low value

per asset or node. However, ubiquitous deployment of sensor

technology at a sufficiently low cost per asset/node in the

distribution system could yield significant improvements in

overall system resilience and stability. A significant driver for

monitoring is the increase in DERs, distribution

interdependencies, and automation. Increased deployment of

power electronic converters is also occurring for grid

interconnection of DER. New sensors for asset health

monitoring of these converters is therefore an area of emerging

importance. In contrast to transmission assets, relatively limited

historic data exist regarding what measurements are critical for

detecting and preventing distribution asset failures. Sensor

development efforts must be coupled with system-level models

and targeted experimental R&D to understand how incipient

failures can best be predicted.

Key measurements: Temperature, chemistry, moisture, oil

level, fault currents, voltage, vibration, ambient temperature,

internal pressure, cumulative operating conditions (stresses

over time), cumulative switch/circuit breaker cycles, tilt/sag

Substations

Substations serve as interconnection points between multiple

high-voltage transmission lines or between those lines and

distribution systems. Substations will commonly employ a

broad range of components, including transformers, circuit

interrupters/breakers, voltage controlling equipment, and power

factor correction devices (e.g., capacitors, reactors, static VAR

compensators), power flow controllers, protection and control

equipment (relays, fuses), voltage and current transformers, and

other instrumentation. With increased renewable resource

penetration and other DERs, regulation and protection devices

are anticipated to experience increased demand and operational

challenges. Increased deployment of power electronic

converters is also anticipated. Substations play a critical role in

the health of the modern electrical power system. Health

monitoring schemes that are increasingly real-time rather than

based upon periodically scheduled inspections can avoid

catastrophic substation-related failures and enable proactive

maintenance programs that minimize disruptions and the

associated social and economic costs.

Key measurement parameters: Temperature, chemistry, oil

level, fault currents, voltage, visual inspection, voltage,

vibration, ambient temperature, internal pressure, cumulative

operating conditions (stresses over time), cumulative

switch/circuit breaker cycles

Transmission Lines

Sensors for transmission line monitoring can enable a utility to

move further toward condition-based maintenance programs

for these lines. While transmission line monitoring

technologies exist, further cost reductions and novel

technologies can enable broader deployment and/or higher

fidelity information at a given cost to increase visibility within

the transmission system.

Key measurement parameters: Tension, sag, temperature,

visual inspection, proximity monitors, leakage currents

Centralized Thermal Generators

Increased cycling of centralized generators, as in fossil-based

power plants, is required with increasing levels of renewable

penetration in the electric grid. Cycling and load following can

accelerate degradation of the materials and components within

these plants. Also, this type of cycling can lead to reduced

efficiencies, greater down time, higher costs of electricity due

to increased need for time-based preventative maintenance

procedures, and potentially even catastrophic failures. A need

exists for new asset health monitoring of these generators to

allow for early detection of potential failures within a power

plant to enable condition-based maintenance and real-time

processing adjustments to reduce potential impacts.

Key measurement parameters: Temperature, strain,

vibration, delamination or spallation, acoustic—audible or

ultrasonic

DERs

DER can also benefit from real-time asset health monitoring.

Ubiquitous deployment of sensor technology at a sufficiently

low cost per DER or node that is capable of health performance

monitoring of these systems can yield significant improvements

in overall system resilience and stability.

Key measurement parameters: Temperature, state of charge,

fault currents, voltage

64

Multi-tier metrics: To address the needs for asset health

monitoring across the modern electric power system

infrastructure, a tiered set of metrics is required that captures

(1) cost, (2) functional performance, and (3) geospatial

characteristics. The latter is needed to identify requirements for

sensing technologies that are able to measure parameters

having spatial characteristics that are consistent with the grid

assets to be monitored. For example, T&D lines are best

monitored by sensor technology platforms with linear

characteristics, whereas substations or specific grid assets are

more suitable for multipoint or areal imaging–based sensor

platforms. The following table provides a summary of the

various grades/levels of performance and examples of cost,

performance, and geospatial characteristics needed across the

various research thrusts described below. In the table, the term

“low grade” refers to lower-cost and potentially lower-

performance solutions typically relevant for distribution level

applications, “high grade” refers to higher-cost and higher-

performance solutions typically relevant for transmission level

or generator monitoring.

Overall metrics for asset health monitoring sensors

Grade Type Cost Performance Geospatial characteristics

Low grade/distribution

level

Minimal costs to enable ubiquitous deployment • Typical sensor cost metrics are <$100/node

(deployed) and communication; <$1–10 is desired in most cases

Adequate, but potentially reduced performance compared with existing transmission-level sensors Proxy-based sensing through indirect parameters measurable through low-cost platforms Compatibility with deployment requirements such as (1) internal to grid assets, (2) medium-voltage distribution lines, and so on Single point— a sensor with a single node

Multipoint— a sensor with multiple discrete nodes Linear—a sensor with linear nodal sampling capability Areal— a sensor with areal nodal sampling capability

high grade/transmission

level

Dramatic cost reductions to increase deployment • At least 10× cost reduction compared with existing

commercial technologies is targeted. • Typical metrics are <$1000/node deployed and with

communication

Comparable or improved performance compared with existing state-of-the-art commercial sensors Compatibility with deployment requirements such as (1) internal to grid assets, (2) high-voltage transmission lines, and so on

High grade/centralized thermal generator

Costs are not the primary driver for technology development because of lack of existing technology ▪ Typical metrics are <$10,000/node deployed and

communication; <$1000 desirable in some cases

New sensor device technology development and deployment Compatibility with operation in extremely high temperatures and harsh environmental conditions representative of fossil- and nuclear-based generation

1: Real-Time Dissolved Gas Analysis Sensors

Real-time DGA sensors can enable early fault detection and

classification for electrical assets in which insulation oil is

employed, including power transformers, underground

transmission lines, and circuit breakers. Lower-cost DGA

technologies, with the following characteristics, need to be

developed for broader deployment to a larger range of grid

assets.

High-Grade/Transmission Level:

Key measurements/metrics: Fully installed cost <$1,000

Performance:

▪ H2, CH4, acetylene, moisture, CO, other

hydrocarbons (levels ranging from 1 to 500 ppm),

▪ Same or better performance as current state-of-the-art

commercial on-line DGAs

Geospatial: Single point

Low-Grade/Distribution Level:

Key measurements/metrics: Fully installed <$100,

Performance:

▪ At least 1 proxy species (H2), preferably multiple

species (H2, CH4, acetylene)

▪ H2, CH4, acetylene, moisture, CO, other

hydrocarbons (levels ranging from 1 to 500 ppm)


2: Grid Asset Internal Temperature

Internal temperature is a key parameter which serves as an

early indicator of fault conditions in essentially all electrical

grid assets, including centralized thermal generators.

Temperature measurements tend to provide insights into natural

degradation and failures of electrical grid assets including

aging, arcing, etc. Lower-cost temperature probes that can be

deployed internal to electrical grid assets need to be developed

including multi-point sensor technologies. High-temperature,

harsh-environment sensor technologies also need to be

developed for centralized thermal generator applications.


Cost: Fully installed cost <$2,000

Performance:

▪ Temperature (ambient to ~100C)

Geospatial: Multipoint, >10 individual nodes


Cost: Fully installed cost <$100

Performance:



High-Grade/Centralized Thermal Generator:

Cost: Fully installed cost <$10,000

Performance:

▪ Temperature (ambient to as high as 1500C)


3: Grid Asset Internal Strain

Internal strain is a parameter that correlates with other proxy

measurements that serve as early indicators of fault conditions

65

in essentially all electrical grid assets, including centralized

thermal generators. Lower-cost strain sensor probes that can be

deployed within electrical grid assets need to be developed,

including multipoint sensor technologies. High-temperature,

harsh-environment sensor technologies also need to be

developed for centralized thermal generator applications.


Cost: Fully installed <$2,000

Performance:

▪ Strain (2 resolution, 100 range)



Cost: Fully installed <$100

Performance:





Performance:


▪ Ambient to temperatures greater than 600C


4: Acoustic and Ultrasonic Vibration Event Detection

Proxy measurements, such as vibration detection, can play an

important role in the indirect identification of early signatures

of events such as faults before catastrophic failure, without the

need for intrusive sensors placed within electrical grid assets.

Applications include detection of low-probability, high-

consequence events that can lead to grid asset failures,

including external impacts or attacks, loose junctions or failing

connections, and arcing or other electrical failures. Depending

upon the event/fault characteristics, vibrations can be detected

and analyzed in the acoustic or ultrasonic range. If coupled

with pattern recognition algorithms, signatures of particular

events/faults can be extracted.

High-Grade/Transmission Level


Performance:

▪ Ultrasonic vibration (10 kHz to 1 MHz)

▪ Acceleration range ( 20g)

▪ Sensitivity (10000 LSB/g)

▪ Acoustic vibration (20 Hz to 10 kHz)

▪ Signal-to-noise ratio ( >60 dB)

▪ Sensitivity (−50 dBFS)


Low-Grade/Distribution Level


Performance:

▪ Ultrasonic vibration (10 kHz to 1 MHz)









Performance:

▪ Ultrasonic vibration (10 kHz to 500 kHz)




▪ Signa- to-noise ratio ( >60 dB)



5:Areal Temperature and Gas Insulation Leak Monitoring

through Imaging

Thermal imaging techniques can be extremely valuable for

real-time areal monitoring of electrical grid assets for detecting

local hotspots in cases where visual access is possible, such as

in substations and near power transformers. However, the high

cost of standard thermal imaging technologies prohibits

widespread deployment. Lower-cost thermal imaging

technologies need to be developed with sufficient areal

range/resolution for broader classes of electrical grid assets.

Emerging imaging techniques can also enable real-time areal

monitoring of leaks of insulation gases in gas-insulated

substations, arcing on transmission lines or within their

equipment, and so on. Early detection of insulation gas leaks is

valuable because of the high global warming potential of

standard insulation gases, such as SF6, combined with the

potential for catastrophic failure if proper insulation levels are

not maintained within the equipment. Low-cost imaging

technologies are proposed with sufficient areal range/resolution

for typical gas-insulated electrical grid assets.



Performance:

▪ Temperature (ambient to ~125C, resolution ~2C)

▪ SF6 concentration (levels above ~100 ppm in air)

Geospatial: Areal (range 300 ft2, resolution 0.06 ft2)



Performance:

▪ Temperature (ambient to ~125C, resolution ~2C)

Geospatial: Areal (range – 300 ft2, resolution 0.06 ft2)

6: Pole Tilt and Line Sag Monitoring

Real-time monitoring of T&D line poles and line sag

monitoring can provide unique insights into the origin of

existing faults, as well as information about where such assets

must be inspected to determine if maintenance, repair, or

vegetation removal is needed. Low-cost sensing technologies,

with capabilities for multi-axis tilt monitoring of lines, need to

be developed for deployment at both the T&D levels.



Performance:

▪ Angle of inclination relative to vertical (0–90 range,

2o resolution)

▪ Angle of twist relative to horizontal reference (0–

360 range, 2 resolution)



Cost: Fully installed <$50, preferably <$5

Performance:

▪ Angle of inclination relative to vertical (0–90 range,

2o resolution)


7: Line Temperature Profile

Local line temperatures can provide information about faults

and failures, as well as important information required for

dynamic line rating on a broader range of transmission and

even distribution line assets. Low-cost temperature sensor

technologies need to be developed, with a particular emphasis

on linear sensor technologies that enable full temperature

profile characterization along the entire length of a line and

with sufficient spatial resolution and measurement range.


Cost: Fully installed <$5,000 per km

Performance:


Geospatial: Linear

▪ Spatial resolution > 6 in.

▪ Maximum interrogation distance > 10 km

66



Performance:


Geospatial: Linear

▪ Spatial resolution >1 in.

▪ Maximum interrogation distance >1 km



Performance:

▪ Temperature (ambient to as high as ~1500C)

Geospatial: Linear

▪ Spatial resolution >1cm

▪ Maximum interrogation distance >10 m

8: Line Acoustic Monitoring

Adverse weather conditions, including wind and storm

conditions, as well as existing faults can introduce acoustic

signals that propagate along T&D lines. Spatially resolved

acoustic monitoring techniques can enable identification of the

locations of conditions or faults that can result in widespread

outages if allowed to persist without intervention. Low-cost

temperature sensor technologies need to be developed, with a

particular emphasis on linear sensor technologies that enable

local identification of the sources of measured acoustic signals

for condition-based maintenance.



Performance:

▪ Acoustic profile (20 Hz to 10 kHz) (coupled with

pattern recognition algorithms)



Geospatial: Linear

▪ Spatial resolution >120 in.

▪ Maximum interrogation distance > 10 km



Performance:

▪ Acoustic profile (20 Hz to 10 kHz) (coupled with

pattern recognition algorithms)



Geospatial: Linear

▪ Spatial resolution > 12 in.

▪ Maximum interrogation distance >1 km



Performance:

▪ Temperature (ambient to as high as ~1500C)




Geospatial: Linear

▪ Spatial resolution >1 cm

▪ Maximum interrogation distance >10 m

#9: Energy Storage (Internal Chemistry)

Energy storage is becoming an increasingly important electrical

grid asset, yet catastrophic conditions can occur in cases when

leaks or other failures of the electrodes, electrolytes, or sealing

are encountered. Chemical signatures of such leaks and failures

can be used for preventative maintenance as well as to prevent

widespread electrical power disruption caused by energy

storage failure. Low-cost embedded chemical sensor

technologies need to be developed with a particular emphasis

on early detection of species that can signify onset failures,

such in Li-ion batteries.



Performance:

▪ Presence of chemical species indicative of failure

onset (HF at 50 ppm, others)


10: Boiler Water Chemistry Monitoring

Existing plants are being required to cycle (on/off) and power

ramp under conditions that were not envisioned when they

were originally designed, built, and deployed. Boiler-water

chemistry is one of the key parameters for these plants that can

provide an early indication of corrosive conditions that must be

prevented to avoid failures or unnecessary costly repairs. A key

requirement involves the ability to monitor chemical

parameters, such as the acid level (pH) of water chemistry,

under high temperature and pressure conditions relevant for

centralized thermal generator boiler applications. Therefore,

real-time elevated pressure and temperature pH sensors need to

be developed.

High-Grade/Centralized Generator Level


Performance:

▪ Real-time pH monitoring (pH range ~4–11)

▪ Temperatures (ambient to as high as 1000C)


67

68

Phasor Measurement Units for Grid State and

Power Flow—R&D Thrusts

PMUs are a critical enabling technology for providing power

system visibility and control capability. They have become

more widely used to measure and time-stamp basic electrical

parameters in modern systems since 2009, but they still require

significant improvements in both performance and cost to

achieve grid modernization goals related to system visibility

and control. The cost-reduction and performance improvement

goals described in the subtopics of this focus area are intended

to catalyze wider and more rapid adoption of PMUs across the

grid and to enable novel dynamic control implementations that

significantly enhance observability, control and reliability.

Key measurements: Voltage, current, frequency, phase angle,

real and reactive power

1: Improve the Dynamic Response and Accuracy of PMUs

Improve the dynamic response of PMU technologies to

significantly improve dynamic grid state measurements and

enable high-speed, real-time control applications (including

automatic controls). This R&D thrust seeks to provide a 1 to 2

order of magnitude performance improvement over the current

PMU state of the art.

Improvements in synchrophasor precision are also needed. In

particular, phase angle differences in distribution systems are

much smaller than in transmission systems. Thus

synchrophasor angle measurements are not adequate with

current PMU technology. As a result, there is a need to develop

PMUs that can accurately capture small differences in phase

angles within distribution systems, especially on the same

distribution feeder. A large percentage of these angle

differences on the same feeder could be less than 0.01 degree

between adjacent feeder locations (nodes). Differences this

small cannot be appropriately captured with current

commercially available PMUs. In addition, low measurement

data rates (60 or 30 frames per second or fps) and long

estimation windows (5~6 cycles) limit the application of PMUs

in some critical grid protection and control applications.

Key measurements: Voltage and current phasors (magnitudes

and angles)

Key metrics:

Target specification: <1 cycle time delay

Measurement rate: > 480 fps

Angle resolution: <0.001 to 0.002 degrees

TVE: <1%

Attributes: Resiliency, flexibility

EGS level: Electrical state

Scope of activity: Develop robust, cost-effective PMU

technology and phasor calculation algorithms with improved

resolution, precision, and dynamic response with pilot-scale

deployment and testing.

2: Lower the Cost of PMUs

Lower the cost of PMUs to enable greater wide-area

deployment and use in both T&D systems and provide more

granular grid visibility and event detection. This effort can also

include multiple product implementations, including substation

use, transmission line monitoring, and integration with existing

assets and original equipment manufacturer power equipment.

Key measurements: Unit cost, installation cost, operating and

maintenance costs

Key metrics:

Unit cost <$500 (transmission system) and <$100 (distribution

system)

Installation cost <$1,000 (transmission system) and <$100

(distribution system)

Operating and maintenance costs TBD (requires analysis)

Attributes: All


Scope of activity: Seek to develop prototypes and system

architectures that reduce total installed PMU costs, including

unit cost, installation, communication-associated costs, and

cyber-physical security-associated costs.

3: Improve PMU Timing Reliability

Incorporate alternative, high-reliability timing methods into

PMU architectures to reduce or eliminate current dependence

on GPS timing signals. The lack of an alternative timing source

to GPS has implications for both reliability (current high

dependence on GPS operational availability) and cyber-

physical security (spoofing vulnerability of GPS signals).

Key measurements: PMU timing, system reliability

Key metrics:

Timing: Short term—IEEE C37.118.1-2011 timing error

compliance

Reliability: 99.999999% timing service reliability

Attributes : Resiliency, flexibility


Scope of activity: System hardware and algorithm design

development with prototype demonstration in pilot-scale

environment

4: Understand and Improve Real Grid Environment

Measurement Performance

Currently the accuracy of PMUs is evaluated using synthetic

signals generated in a laboratory instead of using real electrical

signals. The real electrical signals in the grid are more complex

owing to constant disturbances, interferences, noise, and so on.

Thus, the measurement accuracy of PMUs in the actual power

grid environment is not well understood, particularly for

distributed measurements where the measurement environment

is more complex. In addition, the lack of real measurement

evaluation makes it difficult to verify PMU data across

different manufacturers. This research area seeks to understand

the characteristics of actual grid signals captured by PMUs in

the field and the effect of the actual system on the accuracy of

synchrophasor measurements.

Key measurements: Characteristics of real power grid signals

Key metrics: Noise (signal-to-noise ratio and noise color),

disturbance, interferences level



Scope of activity: Analyze and characterize real electric grid

signals and evaluate their impact on synchrophasor

measurement accuracy

69

70

Novel Electrical Parameter Sensors—R&D Thrusts

With the transition to modern power systems, there is a

heightened need for electrical parameter reporting at faster

rates, higher precision, and greater accuracy—all while

reducing the costs associated with this information. However,

oftentimes, cost reduction is an orthogonal to more accurate or

precise measurement functionality. Novel electrical transducers

can play an impact across a broad range of applications and use

cases across the transmission and distribution system. To

explore synergies and cross-cutting opportunities, the

development and application of novel voltage and current

transducer was the focus for achieving (1) dynamic system

protection, (2) grid asset functional performance monitoring,

and (3) advanced generation controls. Current proposed

research thrusts within these focus areas were developed with a

clear understanding of the current industrial state of the art and

quantitative metrics for new sensing and measurement

technology development. The following table provides a

summary of the various grades/levels of performance and

examples of cost, metrics, and other characteristics identified

across the various novel sensor R&D thrusts that follow.

Metrics for novel electrical parameter sensors

Grade Type Cost Performance Other characteristics

Low grade/distribution level or customer-sited

Minimized costs to enable ubiquitous deployment Typical metrics are <$100/node deployed and communications; <$5–10 is desired in some cases.

Adequate but potentially reduced performance compared with existing transmission-level sensors. Proxy-based sensing through indirect parameters measurable through low-cost platforms. Compatibility with deployment requirements such as (1) internal to grid assets, (2) medium-voltage distribution lines, and others.

Further cost reductions may be achieved via multiple sensors sharing the same communication infrastructure. High

grade/transmission level

Dramatic cost reductions to increase deployment. At least a 10× reduction in costs compared with existing commercial technologies is targeted. Typical metrics are <$2000/node deployed and with communication.

Comparable or improved performance compared with existing state-of-the-art commercial sensors. Compatibility with deployment requirements such as (1) internal to grid assets, (2) high-voltage transmission lines, and others.

Advancements in printed sensors and wireless interrogation of

passive sensors indicate that a suite of novel transducers

capable of being deployed/installed directly onto electrical grid

assets is coming closer to commercial viability. The following

is a list of several novel sensor R&D thrusts identified as future

needs of the electric grid.

1: Fast-Acting Sensors for Fault Detection and Dynamic

System Protection

In a modern power system, fast-acting sensors must detect

electrical abnormalities in a variety of locations. These

locations range from behind-the-meter at customer locations to

bulk power transfer infrastructure and everywhere in between.

With the transition to a modern power system, the grid is

becoming more highly networked to enable two-way power on

its electrical lines. Coupling two-way power flow and the

increase in the number and diversity of grid assets creates a

greater challenge to protect grid assets from disruptions such as

power surges, over and under frequency, over and under

voltages, and harmonics. Fast-acting sensors are needed on the

grid to identify emerging and immediate problems and to

prevent damage to grid assets by deploying adaptable

protection schemes. Following detection of an abnormality,

these sensors also must initiate a broadcast signal or control

response to protect grid assets from damage. These sensors

must quickly transmit their data, so that relays and switches can

be activated to protect grid equipment from damage. Sensors

must be capable of high detection performance (e.g., response

time, accuracy, precision) to meet the requirements of adaptive

protection schemes.

Fault current detection can play a key role in the detection of

fault conditions, potentially including rapid transient fault

currents that can indicate human-caused or natural threats

including cyber-physical attacks, electromagnetic pulses, and

geomagnetic disturbances. Rapid, high-bandwidth fault-current

sensors are proposed, as well as low-cost sensors for ubiquitous

deployment.

Much like fault current detection, under and overvoltage

monitoring of electrical grid assets (including transient

overvoltages), which is an indirect measurement of fault

current, can play an important role in the detection of fault

conditions. Rapid, high-bandwidth under and overvoltage

sensors are proposed, as well as low-cost sensors for ubiquitous

deployment.

71

Real-time sensors are needed on the grid to identify emerging

and immediate problems and to prevent damage to grid assets

by deploying adaptable protection schemes. These sensors must

quickly sense and transmit their data, so that relays and

switches can be activated to protect grid equipment from

damage.

Current metrics: High-Grade/Transmission Level

Fault currents (0.01 nominal rated current to 100× nominal

rated current)

Bandwidth (line frequency to greater than 10 MHz)

Latency (<1 millisecond)

Fully installed cost <$2,000

Current metrics: Low-Grade/Distribution Level

Fault currents (0.1 nominal rated current to 5× nominal rated

current)

Bandwidth (line frequency to greater than 1 MHz)

Latency (<5 milliseconds)

Fully installed cost <$300

Voltage metrics: High-Grade/Transmission Level

Voltage (0.01× up to 5× nominal voltage per unit (p.u.)

Time resolution of voltage change (<1 microsecond)


Total installed cost <$2,000

Voltage metrics: Low-Grade/Distribution Level

Voltage (0.1× up to 2× nominal voltage, p.u.)

Time resolution of voltage change (<10 microseconds)

Latency (<5 milliseconds)

Total installed cost <$300

Frequency :

Frequency measurement accuracy <0.5 milliradians

Phase angle:

Phase angle accuracy within (±0.5× harmonic number)

Harmonic composition:

Individual harmonic amplitudes (accuracy <5%)

Individual harmonic phase angles (accuracy <1%)

Sampling rate (>1000 per 60 Hz cycle)

Total harmonic distortion (accuracy <0.5%)

Cost:

Total installed cost depends on the application, but <$2,000

2: Grid Asset Health Performance Monitoring (Traditional

Transformers)

Asset monitoring for determining equipment heath condition

can be applied to a variety of assets, including generation,

energy storage, and loads, as well as the electrical components

of the power system.

A number of benefits can be derived from improved visibility

of the condition and health of grid assets, including increased

reliability and resilience through prevention of catastrophic

failures of critical assets and implementation of condition-

based maintenance programs as a substitute for run-to-failure

or time-based application requirements of the T&D system.

Traditionally, expensive components such as transformers were

monitored for health condition; while other components, such

as distribution transformers, operated until failure and were

replaced with spares. With the movement toward a modern

power system, it is desirable to monitor the health and

performance of grid assets in greater numbers—which could be

achieved by reducing the cost of asset monitoring sensors.

Key measurements:

Voltage, currents, real and reactive power, phase angle,

harmonics, and THD

Key metrics:

Voltage and current monitoring:

Voltage (up to 5× nominal voltage),

Current (up to 3× nominal voltage)

Time resolution (<1 microsecond)

Sampling rate (>1000 per 60 Hz cycle


State of magnetization of the core:

State of magnetization as function of operating conditions.

Magnitude and distribution of main, leakage and zero sequence

flux:

Flux as function of operating conditions.

Total losses and localized loss densities in windings and

different structural parts of the transformers:

Losses as function of operating conditions.

3: Performance Sensors for Next Generation Devices

Next-generation devices include power conversion devices

(solid-state transformers, energy storage, and DER). Next-

generation transformers will require sensors that do not rely

predominantly on gas sensing. A greater market share of

transformer-less power electronics will be used for bulk power

transfer. Examples of such transformer-less power electronics

are devices such as FACTS, STATCOMs, UPCS, and similar

ones. These devices are based on solid-state switching devices

and require fundamentally different monitoring approaches.

Rapid penetration of DER resources, including conventional

and renewable generation, call for increasing installations of

energy storage (ES) for multiple applications and benefits (such

as providing power from renewables when the source is

momentarily unavailable—e.g., solar PV when clouds block

the sun). Accurate and timely information is needed for

electrochemical states of the ES for both controls and, even

more important, safety status.

Key measurements for power conversion elements: Current,

voltage, current derivative, voltage derivative, frequency

content, phase angle, fault currents

Key metrics:

Voltage, currents, real and reactive power, phase angle,

harmonics, and THD, pulse width modulation (PWM)

diagnostics info

Voltage and current monitoring: (same as for ”traditional”

transformers)


72


Phase balance/imbalance (accuracy <0.5%)

PWM— accuracy and balance (accuracy <0.5%)




Phase angle accuracy (±0.5 × harmonic number)

Harmonics composition:

Individual harmonic amplitudes (accuracy <5%)

Individual harmonic phase angles (accuracy <1%)


THD (accuracy <0.5%)

Latency (<1 ms)


Cost (<$10 per generator)

Key measurements for energy storage: State of

charge/discharge, rate of charge/discharge, depth of

charge/discharge (per cycle, and average over life), pressure,

outgassing state/status, cumulative number of cycles (life),

cumulative charge/discharge information (lifetime kWh/MWh),

temperature (to avoid temperature run-off)

Key metrics:

Normalized state of charge/discharge, 0–100% (accuracy <1%)


Rate of charge/discharge, % (accuracy <1%)


Depth of charge/discharge, % (per cycle, and average over life)

Reporting frequency = no less than once/day

Cumulative number of cycles (life), (accuracy = number of

cycles)


Cumulative charge/discharge information (lifetime

kW/h/MW/h),


Key measurements of DER: Smart functions enabled (e.g.,

volt-var control), voltage, current, frequency, phase angle,

power flow, line losses, line loading, line/segment impedance

Voltage and current monitoring: (same as for ”traditional”

transformers)



Phase balance/imbalance (accuracy <0.5%)

PWM—accuracy and balance (accuracy <0.5%)




Phase angle accuracy within (±0.5× harmonic number)

Drivers: Reliability, flexibility, resiliency, sustainability

4: Derivative Sensors

Similar to rate of change of frequency (ROCOF), derivative

sensors for voltage and current may be very useful for utilities

for monitoring of dynamic operating states. Applications and

drivers of this thrust are fast detection and response to an event

that is much faster than line (60 Hz) frequency. Applications

can range from steady-state power flow and state estimation to

detection of fast transient events.

Key measurement parameters: Current derivative, voltage

derivative, frequency content, phase angle, fault currents, cost,

ease of installation and maintenance, safety.

Key metrics:

Change in current with time (dI/dt):

Accuracy: Depends on ampacity rating of monitored

application, between 0.1A/ms and up to 10A/ms

Bandwidth: 1 kHz–1 MHz

Latency (<1 ms)

Total installed cost <$2,000

Change in voltage with time (dV/dt):

Accuracy: Depends on monitored application; better than %/ms

or % p.u./ms

Bandwidth: 1 kHz–1Hz

Latency (<1 ms)

Total installed cost < $2,000

ROCOF, or df/dt, for fundamental and (optionally) for

harmonics

Accuracy: <0.05Hz/s

Bandwidth: 1 kHz–1 MHz


Total installed cost < $2,000

5: Broadband Frequency-Selective Current Sensor

Present current-monitoring and current-sensing technologies do

not distinguish between frequencies contributing to current.

Frequency-selective current sensing and monitoring would be

very useful for monitoring harmonic contribution to current, as

well as monitoring of transient dynamics.

Key measurement parameters: Current, frequency content,

phase angle, fault currents

Key metrics: Accuracy, dynamic range, rate of sampling,

latency, cost, ease of installation and maintenance, safety

73

Voltage and current sensing:



Sampling rate (>1M samples per 60 Hz cycle


Phase angle accuracy within (±0.5º × harmonic number)

Frequency metrics:

Frequency resolution (better than 1 kHz)

Frequency bandwidth/selectivity (better than 10 kHz)

Cost (<$10 per sensor)

Drivers: Resiliency, flexibility


Scope of activity: Sensor technology development at

laboratory scale, followed by pilot-scale deployment and

testing, ultimately technology transition to industry

6: Maturation of All-Optical Transducer Technologies

All-optical transducers use electro-optic or magneto-optic

effects to sense the voltage or current signals. Some of their

excellent properties include complete electrical isolation, small

size/lightweight, and DC measurement capability. However,

technology maturation is needed to address the issues of cost

effectiveness, resistance to temperature change and vibration,

safety, and long-term stability.

In addition, integrating wireless communication and embedded

data analysis functions onto the transducers needs consideration

and R&D.

Key measurement parameters: Voltage, current, smart

functions enabled (volt-var, etc.), voltage, current, frequency,

harmonics and THD, phase angle, power flow, line losses, line

loading, line/segment impedance.

Key metrics: Key metrics required for this functionality are

listed in research thrusts mentioned above. However, for this

thrust, the same metrics need to be reproduced using all-optical

technologies. Accuracy, dynamic range, cost (<$$$/kVA), ease

of installation and maintenance, safety.



Scope of activity: Optimization to address costs, temperature

and vibration resistance issues, development of

testing/calibration procedures, ultimately technology transition

to industry for large-scale deployment

7: Behind the Customer Meter Sensing

The focus is on transducers creating actionable information

from all the new smart devices that may be installed behind the

customer meter location.

Gap: Lack of knowledge and detection of new installations

behind the meter that are occurring without utility knowledge.

Such devices and installations may be leading to bidirectional

power flow without utility knowledge. Additionally, these

devices and installations may be injecting additional harmonics

or frequency noise onto the distribution system, which in turn

may lead to reductions in the lifetimes of other utility assets

(such as secondary transformers).

Additionally, a “watchman” application may be needed for a

utility to verify that, for example, a behind-the-meter

customer’s inverter is generating watt/Var as per an

interconnection agreement.

Key metrics: Key metrics required for this functionality are

listed in the research thrusts mentioned above. Some of the

sensing solutions needed may already exist. What is definitely

missing is system integration of the sensors.

Potential solution: A possible solution may be a device similar

to a microinverter, which monitors the performance of several

devices and broadcasts this information to the utility. A

possible smart outlet that can collect power and power quality

information is another example. A complete solution would be

a smart meter, which not only provides revenue information but

also provides power and power quality information for all

devices at the customer’s interconnection location.

74

75

End-Use/Buildings Monitoring—R&D Thrusts

Smart meters provide utilities with the ability to monitor energy

consumption at end-use loads and enable monitoring of the

distribution system for steady-state operation. However, the

monitoring of a high penetration of DERs like energy storage

and renewable generation located in the distribution system

requires much faster and higher resolution (e.g., milliseconds)

sensors for control, understanding system dynamics, and

performing islanding and resynchronization of

microgrids/nano-grids. These sensors should be able to provide

the data needed for advanced applications, such as seamless

islanding and resynchronization, transactive controls, and so

on.

As various DER and energy storage technologies advance and

become more affordable, customers will have the ability to

control their energy production and consumption and become

active participants in the distribution network. To enable

optimal building operation, interactive and intelligent multi-

component integrated sensors need to be developed for

comprehensive self-learned/adaptive controls.

Key measurements: Frequency, phase angle, currents, voltage,

real and reactive power, power factor, power quality, humidity,

air quality, luminance, air flow, refrigeration liquid, occupancy.

1: Development of High-Resolution Building-to-Grid

Sensors

With the high penetration of DER and electricity energy

storage at the end use, much faster and higher-resolution (e.g.,

millisecond) sensors (e.g., current/voltage or micro-PMU) are

needed for control, system dynamics and possible

home/building islanding operation as well as resynchronization.

The measurement accuracy of sensors in the real measurement

environment needs to be understood. The measurement

consistency of sensors between different manufacturers in these

real measurement environments needs to be quantified.

Key measurement: Frequency, phase angle, currents, voltage,

real and reactive power, power factor, power quality

Key metrics:

Current and voltage:

▪ Voltage (up to 2× nominal voltage),

▪ Current (up to 2× nominal current

▪ Measured data resolution (milliseconds level)

▪ Measurement accuracy (error < 0.5%)

▪ Fully installed cost (<$500)

Frequency, phase angle, real and reactive power, power factor,

power quality:

▪ Calculated based on the current and voltage measurement

▪ Measured data resolution (milliseconds level for

frequency and phase angle, seconds level for real and

reactive power, power factor and power quality)

▪ Measurement accuracy (error < 0.5%)

Drivers: Reliability, resiliency, security, efficiency


2: Development of High-Accuracy and Low-Cost Building

Efficiency Sensors

Currently, temperature, humidity, luminance, air quality,

pressure, air flow, refrigeration liquid, and building occupancy

are measured separately by corresponding sensors, which are

high in cost and power consumption and low in accuracy. In

addition, they don’t communicate/share data. Future multi-

ensor integrated measurement devices that are self-powered,

integrated, interactive, and intelligent need to be developed for

adaptive controls.

Key measurements: Temperature, humidity, air quality,

luminance, air flow, refrigeration liquid, occupancy

Key metrics:

Temperature:

▪ Measurement data resolution (0.1F)

▪ Accuracy (error <1F)

▪ Fully installed cost (<$10/node)

Humidity:

▪ Measurement data resolution (<0.5%)

▪ Accuracy (error <2 %)


Luminance:

▪ Measurement data resolution (accurately report light

levels for the building type to enable dimming between 30

and 70% of full to no light level for the space)


Air quality, Air flow:



Occupancy:

▪ Measurement data requirement (binary level for discrete

control of lighting loads, zone-level occupancy with >90%

accuracy to control ventilation based on occupancy)


▪ Fully installed cost (<$50/ heating, ventilation, and air-

conditioning zone)

Self-powered:

Battery size (enable mean time between charges of >72 hours)

3: Development of Intelligent Functions for Integrated

Multi-Sensors

Electricity use, temperature, luminance, air quality, building

occupancy, and so on are measured by different pieces of

equipment and typically are not correlated to perform advanced

functions like FDD of building equipment. Future multi-sensor

integrated measurement devices that are self-powered,

interactive, and intelligent need to be developed for

comprehensive self-learned/adaptive controls.

Key measurement: Frequency, phase angle, current, voltage,

power factor, power quality, temperature, humidity, air quality,

luminance, air flow, refrigeration liquid, occupancy

Key metrics:

Same as for Research Thrusts 1 and 2

Adaptive controls for transactive energy:

▪ Energy cost savings (>10%)

▪ Fully installed costs (<$200)

Self-learning for load management:

▪ Energy cost saving (>10%)

▪ Fully installed costs (< $200)

76

77

Weather Monitoring and Forecasting—R&D

Thrusts

This focus area includes high-priority research thrusts for

weather sensing devices with quantitative metrics where

appropriate. The first R&D thrust deals with upcoming

innovative and low-cost technologies that need significant

R&D for successful integration. The second R&D thrust deals

with the requirement of newer devices for advancing the state

of the art. There are additional research thrusts related to

utilization of weather data for advanced modeling, which

appear under the data-driven grid modeling or analytics area.

Weather monitoring and forecasting is relevant to both

electricity consumption and renewable (wind and solar) power

generation. Increasing penetrations of weather-dependent

renewable energy sources are making weather sensors even

more important for monitoring and predicting DER generation.

Installed capacities of solar photovoltaic (PV), concentrating

solar power, and wind energy have grown significantly in

recent years, so that they have a significant impact on

generation profiles. Grid integration of these renewable energy

systems now and in the future benefits from the operational

awareness provided by real-time sensing of both wind and solar

resources and energy production, as well as forecasting from

weather prediction over time scales from 0–5 minutes to 24–48

hours ahead.

Additionally, weather or ambient conditions monitoring is

important for forecasting consumption, accurately modeling

loads, and forecasting the states of interdependent

infrastructures such as transportation and water and gas

systems for a resilient economy. Electricity consumption is

closely tied to the weather, as heating and cooling can be major

components of electricity demand. Temperature and humidity

are key considerations in load forecasts and usage.

Additionally, the transfer capacity of transmission lines

depends on temperature.

Key measurement parameters: Wind speed, wind direction,

temperature, humidity, soil moisture, water turbulence

(offshore wind), irradiance (global horizontal irradiance or

GHI, direct normal irradiance or DNI, and diffuse irradiance),

spectral components, cloud motion, barometric pressure,

precipitation, lightning, icing, renewable power generation

EGS levels: Topological state, component state, building state,

ambient state, convergent networks

1 Integration and Testing of Innovative Low-Cost Weather

Sensing Technologies

There is a dearth of weather sensing technologies deployed at

spatial resolutions sufficient for grid modernization. Thus,

challenges exist related to adequate characterization of spatially

resolved renewable resources and building loads. For wind,

there is inadequate sampling of the lower atmosphere required

for weather models to forecast many of the atmospheric

phenomena that affect wind power production. For solar, there

are not enough high-quality, low-cost sensors available for

verification, observability, initialization, and development of

irradiance and power forecast models. Thus, integrating and

testing of innovative and low-cost weather sensors is a high-

priority research thrust that requires considerable R&D.

Upcoming innovative applications such as lidar-based sensing,

unmanned aerial vehicles (UAVs), all-sky cameras, PV-

integrated reference cells, narrow band photodiodes, and LEDs

need extensive research for their integration, calibration, and

customization for each geographic location. For example, the

Arable Mark device is a low-cost multi-parameter sensing

device based on the LED principle. It has a unique suite of

sensors to measure the downwelling and upwelling shortwave

solar resource, longwave radiation, humidity, air temperature,

and ground temperature. It is also equipped with seven

downward- and upward-facing narrow-band spectrometer

channels that measure spectral radiation and surface spectral

reflectance. Although it is currently used for agricultural

applications, much research is needed to investigate its

usability and adaptability for grid use cases. Another sensor is

the low-cost scalable security camera. Some of these devices

can be integrated with robust communication and time

synchronization capabilities that are relevant for enhancing grid

observability.

The evaluation of weather sensors for the modern grid requires

continuous collaboration among various stakeholders to create

awareness and devise low-cost pathways to integrate these

technologies.

Key metrics:

Parameters Key metrics for innovative technologies

Broadband

irradiance

components: GHI,

DNI, diffuse

horizontal

irradiance (DHI),

plane of array

(POA), albedo

Photodiodes, reference cells (<$300 cost,

lifespan as long as the distributed panels),

shadow band devices (at least 50% cost

reduction from current ~$14K, increased

lifespan of shadow band motors for high-

frequency measurements)

Spectral

components,

surface albedo,

aerosol, moisture

Arable pulsepod (<$600)

Clear sky index,

cloud

characterization,

cloud base and top

heights

Sky imagers and security cameras

(<$100), satellite sensing (low latency

<1 min real-time data), lidar-based cloud

height estimation (50% reduction in the

current costs of ~$24K)

Wind profile

Scalable radar and sodar based

technology deployments for high-fidelity

profiling (cost reduction from current

915 MHz (~$600K) and 449 MHz

(~$300K))

Attributes: Enabling scalability for grid futures with high

penetration of DER; improved energy forecasting, solar

observability, and grid situational awareness; probabilistic

uncertainty characterization of variable renewable generation

and its ramps

Scope of activity: (1) Private-public partnerships for

integration, comprising innovative sensing vendors, robust

communication and data logger entities, national labs,

academia, and utilities/independent system operators (ISOs).

Prototype validation of low-cost camera integration and

calibration for various grid edge locations.

78

2 Development of Devices for Enhanced Weather

Observability

Weather phenomena are governed by fundamental physics.

Models help to simulate and understand their evolution.

Weather sensors, apart from providing real-time measurements,

also help in initializing and validating these physics-based

models. Unlike the installation of other in situ sensors, weather

sensing technologies face highly versatile conditions and

variations due to the uncertainties in geographical location,

elevation, and local (microscale) and mesoscale weather

phenomena. There is a constant need to capture higher spatial

and temporal resolution information for application-dependent

parameters to improve forecasting and grid-edge resource

observability. Advanced sensors coupled with accurate

calibration capabilities could enhance the dynamism and

observation accuracy under various conditions and could

improve model initialization and reduce prediction

uncertainties. For example, sensors or a combination of these

sensors with multi-parameter sensing capabilities are needed

to improve forecasts of clouds and therefore of surface

irradiance. Getting the irradiance components right (GHI, DHI,

DNI, and POA) is key to integrating higher shares of solar PV

resources. Consequently, estimating the soil moisture

accurately is critical for forecasting the development, evolution,

and dissipation of clouds. Additionally, soil moisture

information provides insights into the probability of flood

conditions. Also, various instruments measuring solar radiation

are not adequately maintained (e.g., kept clear from tree

branches or other shading, cleaned, calibrated, or maintained).

Therefore, alternate capabilities are required to provide

redundant measurements.

Key devices and target metrics:

1. Sensors to measure surface albedo and spectral solar

components more accurately in different terrains, that are

synchronized with multiple parameters relevant for PV

production and plant operational status (e.g., snow detection,

hot spots, panel temperature, undesirable shading). Some

examples include multiple narrow-band LEDs (~$10 per

installation) and filtered photodiodes.

2. Sensors to accurately measure precipitation and soil moisture

to improve prediction of clouds and potential for flood events.

Conventional microwave-based active sensing technologies are

expensive, and satellite-based sensing has lower temporal

resolution for this application. Innovative technologies like

passive sensing using ultra-high frequency radio frequency

identification devices promise lower cost solutions.

3. A new detector for the Absolute Cavity Radiometer for use

as a primary instrument for calibrating devices measuring solar

radiation. There are no instruments currently available in the

market, as high-accuracy detectors were previously hand

manufactured. A new commercially viable design needs to be

developed. This detector requires an accuracy of 0.3% or better.

4. Portable calibrating devices for distributed and remote

applications. Current calibrations are undertaken only in

laboratories, and there is a need for a portable calibration

device that is capable of onsite calibration and is traceable to a

world reference.

5. Calibration capabilities for digital radiometers need to be

developed, as current capabilities can handle only analog

devices. This will enable localized and in situ calibration

instead of a time-consuming and expensive calibration process

available only at manufacturers’ facilities.

Attributes: Improving energy forecasting, scalable deployment

of sensors for smart grid, grid edge observability, measurement

reliability, high-fidelity characterization of solar spectrum at

different locations, calibration standard, low latency (<1 min

for post-event contingency reserves, <5–15 min. for regulation

reserves and ramping products)

Scope of activity: (1) Public-private partnerships to create and

integrate these sensing technologies. (2) Impact and value

proposition study to understand the spatial and temporal

resolution needs from weather sensors for advanced grid

modernization use cases. (3) Development of portable, low-

cost, innovative measurement and calibration technologies

working with sensor manufacturers. (4) Working with research

laboratories to develop high-accuracy primary calibration

devices.

79

80

COMMUNICATIONS

Distributed Communications—R&D Thrusts

Distributed communication is viewed as a promising solution

to tackle the challenges from large-scale deployment of

distributed sensors in the future grid. This focus area targets an

architecture design for distributed communication and an

analysis of its impact on the operation and control of the

electric power grid in terms of various applications.

The Distributed Architecture working group has gathered a

variety of communication architectures that vendors are

proposing—or have sold—to electric utilities specifically and

energy delivery system end users in general. While many such

architectures are being promoted, there are three key

fundamental underpinnings to a next-generation grid-centric

distributed communication architecture that need to be

addressed: IIoT/IoT, wireless spectrum congestion

management, and cyber-physical security.

In addition, the utility’s communication network forms the

transport fabric upon which sensing measurements and control

signals rely. Seemingly ever-advancing technologies must be

readily integrated into such a communication fabric.

Key operational parameters: Robust, cybersecure support of

multiple communications technologies and protocols. Seamless

integration into existing utility networks. Forward looking

technology to advance the IoT and related sensing technologies

and utility communication core fabric networks.

1: Develop Compendium of (Principal) IT/OT Network

Architectures

Utilities from co-ops to investor-owned utilities rely on

communication network designs provided by vendors and/or

best practice guides. While commonality exists across various

designs, the next-generation distributed communication

architecture(s) for energy delivery systems must provide a

wider range of operational skills than previous/current designs.

The ad-hoc adoption of IoT/IIoT devices and systems into all

facets of utility operation with simultaneous integration of

various communications technologies places restrictions on

cost-effective implementation of the fabric and associated

devices.

Key measurement parameters: Cost, performance,

complexity of network elements

Key metrics:

Ease of integration of designed distributed communication

architectures by project’s utility Tech Advisory Board.



Scope of activity: Architecture development at laboratory

scale, followed by pilot-scale deployment and testing and

ultimately technology transition to industry

2: Spectrum Management, 5G and Cybersecurity

Related activities in spectrum congestion management should

be leveraged—specifically, the Networking and Information

Technology Research and Development Shared Spectrum work

in cellular 5G (and future) transport, and cybersecurity projects

(e.g., Cybersecurity for Energy Delivery Systems)—to address

increasing needs for high data throughput, lower latency,

varying data rates, multiple parameter/class of information, and

transport throughout utility service areas. Resource allocation

schemes under dynamic scenarios should be designed and

developed, and optimization techniques can be leveraged to

facilitate the objective.

Key measurement parameters: Multiple parameter/data

sequence throughput, latency, spectrum interference ratio,

cyber testing using best practice guides and methods (ICS-

CERT)

Key metrics:

Estimated cost of system deployment, ease of integration into

legacy networks, reliability (>99.999%. Providing the

throughput required by the corresponding smart grid

application. Interference management to acceptable SINRs

(signal to interference plus noise rates) corresponding to

specific radio frequencies and data rates. Overall spectrum

utilization and performance satisfaction of different smart grid

applications. End-to-end overall latency (as low as 1 ms).

Attributes: Reliability, resiliency, security.

EGS level: Component state, convergent network state,

electrical state

Scope of activity: Architecture technology development at

laboratory scale, followed by pilot-scale deployment and

testing and ultimately technology transition to industry

3: Integration with Multiple Project Sensor Development

and Distribution Grid Asset Working Groups

Multiple projects involve developing sensors and systems with

varying time scales and measurement transport needs. The

objective is collation of the projects’ needs for architecture

communication backbone implications (wireless, wired,

optical).

Key measurement parameters: Latency, data throughput,

multiple communication technology integration ported to utility

network fabric and SCADA core.

Key metrics:

Utility IT/OT department acceptance of multimedia

communication architecture, proven cybersecurity interrogation

and operation across scalable architecture


EGS level: Component and networking states

Scope of activity: Technology development and demonstration

at laboratory scale, followed by pilot-scale deployment and

testing and ultimately technology transition to industry

81

82

Communications and Networking—R&D Thrusts

With the fast development of new communication and

networking technologies, especially the IoT and 5G, it is worth

investigating how to leverage these new grid modernization

breakthroughs to support large-scale deployment of distributed

sensors. The first important issue is to identify the inadequacy

of the existing communication and networking techniques used

for sensing and measurements in the power grid, which serves

as the motivation for investigating and deploying new

technologies. To leverage the emerging IoT technologies, one

important task is to capture the properties of power system

operation and control, which is different from the IT domain. In

addition, new networking technologies (e.g., software-defined

networking [SDN] and network function virtualization [NFV])

can be applied to address the challenges of scalability, diverse

quality of service requirements, efficient network management,

and reliability and resilience. Another challenge is the

interoperability among diverse items of equipment and

standards. Tackling this issue could not only make the modern

electric grid compatible to legacy systems existing for decades,

but also provide an efficient solution for integration of future

systems. This focus area includes research thrusts that facilitate

the development of the interoperability solution.

Key objective: Develop communication and networking

technologies to support large-scale deployment of distributed

sensors in grids

1: Leverage IoT Technologies in Power System

Communications

Emerging IoT and 5G communication technologies have

potential for application in the electric power grid to tackle

several challenges, especially applications for sensing in

distributed system environments, which have several features

in common with IoT applications. On the other hand, power

system operation and control have unique properties that are

different from scenarios of general IoT solutions. These

characteristics should be integrated in designing

communication solutions for sensing and measurement in

power systems by leveraging IoT technologies.

Key metrics:

▪ End-to-end overall latency (as low as 1ms)

▪ Reliability (>99.999%)

▪ Throughput to meet the corresponding smart grid

application

▪ Power consumption to meet power options of the

corresponding device (for battery-powered devices, >10

years of battery-replacement)

▪ Communication range to meet corresponding smart grid

applications

▪ Number of devices in the network/cluster (scalability up to

5 million nodes)

▪ Robust security (compliance with NERC Critical

Infrastructure Protection, or CIP, standard)

Attributes: Reliable, secure, affordable, flexible

EGS level: Electrical state, convergent network state

Scope of activity: Conduct collaborative studies by academia

and national labs. These include theoretical analysis and

simulation case studies, followed by facility testing by industry.

2: Networking Technologies for Scalability Issues while

Satisfying Diverse QoS Requirements

The large-scale deployment of distributed sensors raises the

issue of scalability. A hierarchical architectural design is

adopted with several tiers. The new networking technologies

need to simplify the local control at each tier and provide

coordination among tiers to reduce response time and

operational cost. Meanwhile, the quality-of-service

requirements for different applications/services should be

satisfied by allocating the network resources optimally and

dynamically. The SDN technologies, which provide global

visibility of the network, could be leveraged to facilitate the

optimal resource allocation to tackle these challenges.

Key metrics:

▪ Scalability (up to 5 million nodes)

▪ Quality-of-support support for various smart grid

applications (e.g., for latency as low as 1 ms for protection

application)

▪ Support heterogeneous communication technologies

Attributes: Reliable, secure, flexible


Scope of activity: Conduct studies by academia and national

labs to design the methodologies, followed by collaborative

studies with industry to evaluate and validate the methods.

3: Efficient Network Management to Support New and

Dynamic Services

The future electric grid will enable highly dynamic and “plug-

and-play” system functionalities with large-scale integration of

distributed resources and the associated sensing and

measurement devices. As a result, new services with dynamic

features will prevail which pose challenges to network

management. The emerging networking technologies, e.g.,

SDN and NFV, provide viable solutions to tackle the

challenges. However, in applying them to the electric power

grid, the features of the physical power system should be

integrated.

Key metrics

▪ Support for dynamic network services (plug-and-play

enabled)

▪ Support for adaptive scheduling and resource allocation

▪ Ensuring overhead of network management protocols

satisfy end-to-end latency requirements (as low as 1ms)

Attributes: Affordable, flexible


Scope of activity: Conduct collaborative studies by academia

and national labs for methodology development and simulation

case studies, followed by facility testing by industry.

4: Reliability and Resilience Enabled by Networking

Technologies

The self-healing properties of the communication network

provide reliable and resilient solutions to incidents caused by

either faults or malicious attacks. The self-healing scheme aims

to use the network resources to find alternative paths to enable

communication functionalities to respond after incidents on the

sensor networks, which should be addressed by the networking

technologies. New networking technologies such as SDN have

advantages in terms of global visibility and controllability,

which can be used to design self-healing schemes to enhance

the reliability and resilience of communication networks. The

cyber-physical features of both sensing applications and

communication networks should be considered in the design.

Key metrics:

▪ Reliability (>99.999%)

▪ Resilience—There is no consensus on the definition of

resilience; some quantifications are suggested:

- Minimum node density/neighboring nodes required

to keep the network alive

83

- The extent of loss a network can tolerate and still

provide a certain percentage (e.g., 90%) of service or

critical services.

▪ Security (compliance with NERC CIP standard)

Attributes: Reliable, resilient



labs to develop the algorithms, followed by collaborative

studies with industry to evaluate and validate the methods.

5: Large-Scale Co-Simulation of Cyber-Physical System

Integrating Interoperability Solution

Co-simulation of communication systems and power system

operation and control is a viable tool for testing and validating

interoperability solutions. As distributed architecture (e.g.

OpenFMB framework) pushes the intelligence to the grid edge,

there are several technical challenges in the communication

systems—e.g., time synchronization of local communication,

routing difficulties, and scalability issues. These issues will

also impact the performance of the various power system

applications based on sensing and measurements. A large-scale

co-simulation tool can help to evaluate interoperability solution

performance and study its impacts.

Key metrics:

▪ Scaling from microgrids to a feeder to multiple feeders at

a substation to inter-substation interactions

▪ Heterogeneous hardware such as fiber, copper, power line

carrier, mesh networks, point-to-point radios, Long-Term

Evolution cellular, and maybe someday GHz cellular

▪ Multiple standards and protocols supported

▪ Suitability for the distributed architecture

▪ Adaptability to use cases regarding sensing and

measurements

Attributes: Reliable, secure, flexible, resilient



labs to develop co-simulation tools, followed by collaborative

studies with industry to evaluate and validate interoperability

solutions.

84

85

DATA, ANALYTICS, AND MODELING

Big Data Management—R&D Thrusts

With increasing complexity comes increasing need for holistic

system insight. It is no longer sufficient to silo parts of the data

management system and operate them independently, as there

is increasing interconnection between different parts of the

power system from DERs and distributed controls. Advanced

analytic algorithms have capabilities that far exceed existing

methods, but they require data from multiple domains and

multiple measurement areas, potentially synchronized and of

sufficient quality for analytics. The technology for collecting

and ingesting data must be considered, allowing for the

application of advanced analytics with the operator at the top of

the chain making/guiding (in the case of computer

recommendations) decisions about how to best operate the grid.

The massive amount of available data and analytics need to be

distilled down to simple, easy-to-use displays that give

operators the information they need to do their challenging jobs

effectively.

The power grid is becoming more highly networked as it

transitions to a modern power system with key features such as

two-way power flow, distributed generation and storage, and

responsive loads. As a result of this high degree of

connectivity, there has been a significant increase in both the

volume and variety of data created to monitor and control the

power system. These data represent a significant opportunity

for existing and future applications that can intelligently

operate on such a diverse data set. But for these applications to

be successful, the data must be maintained in a coherent

fashion; and it must be accessible both from a technological

perspective through user and machine interfaces, and by being

be organized so as to be understandable and coherent.

Key objectives: security, maintainability, reliability,

accessibility, cost.

1 Data Access and Interfaces

There are many uses and users of the data produced by

sensing/measurement systems on large power systems, and

each of these uses may impose a different set of constraints on

data access mechanisms. For example, some applications

require access to large amounts of historical data, whereas

others require access to small amounts of recently generated

data, but at very high rates. To be truly useful, a data

management system—or suite of systems—must provide

mechanisms for satisfying the constraints of a variety of

existing data access requirements while maintaining the

flexibility to support future applications. Research efforts must

enumerate a reasonably comprehensive set of existing data

access requirements, predict future data access trends, and

propose data management architectures that will satisfy both in

a cost-effective manner.

A key source of errors in software applications is in the

interfaces between applications. In this case, many different

systems collect the data and must present them in a consistent

fashion to an analytic application. These interfaces need to be

simple, reliable, and standardized. Otherwise the cost of

maintaining and operating a system would greatly exceed any

potential value.

Key metrics: Latency (<1 ms), cost, storage, ease of

installation and maintenance, ease of use, flexibility,

standardization, number of language bindings.

Attributes: Resiliency, flexibility, security.

EGS level: All

Scope of activity: Enumeration of detailed requirements of

primary applications and projected future applications

Characterization of value-based metrics for evaluating

commercial/ specialized solutions. Identification of data

sources and ingestion methods and requirements. identification

of collection technologies and storage solutions, as well as

valuation metrics. Interaction with the communication systems.

2 Data Organization, Visualization, and Fusion

The wide range of data types and data rates originating in large

power systems stretches the capabilities of traditional tools for

organizing data. These tools must support data sources ranging

from short bursts at rates of several kilohertz to one-off manual

data entry. Despite the great variety in rates and content, future

data analytic systems may be able to make use of all existing

and future sources. To support these applications, the data must

be organized in a consistent yet flexible manner and must be

protected to varying levels. Research efforts must identify

applicable schemes to successfully manage and archive the

wide range of existing and future data sources. The human

electric grid operators play a key role in assessing and

managing the grid. To be effective, advanced applications need

to be accessible, trusted, and easily understandable by these

grid operators. Visualization tools and other operator tools must

enhance the abilities of the grid operators to operate the grid in

an effective and reliable manner, both under normal

circumstances and under stress.

Key metrics: Ease of setup, cost, ease of access, flexibility,

management requirements.

Attributes: Resiliency, flexibility, coherency, security

EGS level: All

Scope of activity: Enumerate organizational requirements and

value. Evaluate existing standards and technologies. Establish a

recommended set of best practices and standardized solutions.

86

87

Analytics Support and Integration—R&D Thrusts

Evaluation and maintenance of grid health currently depends

on a centralized, deterministic approach in which data are

collected and analyzed, and some control action is then taken.

In contrast to traditional centralized grid data monitoring and

analysis, building component health relies on a decentralized

analytic approach in which each building component is

monitored and analyzed individually. For performance and

reliability reasons and the large scale of some potential

applications, there is a need to support distributed analytics and

control algorithms across the grid. Efficient and accurate data

management systems must be in place to ensure that data are

distributed where needed on time and reliably and that the

results are consistent and accurate. This needs to be done in a

manner that delivers consistent value in a secure fashion to be

economical for utilities and customers.

Mere availability of more data will not, by itself, lead to changes

in grid visibility, security, and resiliency. To create the predictive

and prescriptive environment required to enable new markets and

transactions for customer revenue and a reliable grid, the data

must be collected, organized, evaluated, and analyzed using

sophisticated pattern-detection (i.e., incipient failure analysis can

have subtle signatures recognizable only by advanced analytics)

and discovery algorithms to provide actionable information

allowing operators to reliably manage an increasingly complex

grid.

Key objectives: Accuracy, performance, security, value

1 Analytics Integration and Platform Development

Characteristics of the distribution grid that make it daunting for

conventional analysis but ideal for application of machine

learning are randomness of customer behavior, high nodal

volume, lack of useful metadata, and the number of unknowns

such as grid topology and availability of behind-the-meter

resources. Fundamental research is needed to integrate

advances in existing machine learning techniques and develop

platforms on which new analytics can be deployed that account

for power-systems physics and variability at the building-to-

grid interface, are secure with low computational burden, and

are easily deployed. Analytics developed through this work

must have integration capabilities through unified data models,

with upper hierarchical utility systems, without the introduction

of further information quality issues.

Key metrics: Latency, reliability, correctness, cost

Attributes: Resiliency, sustainability, reliability, flexibility

EGS level: Component state, electrical state, ambient state,

topological state

Scope of activity: Development, review, and demonstration of

distributed analytics platforms that draw upon multivariate

measured data to enable applications, and demonstration of

integration of distributed data layers to upper centralized

architecture

2 Data Preparation and Evaluation (Validation, Quality

Assessment, Conditioning/Correction)

While new sensors and data sources for the grid represent a

valuable source of information, these streams will inevitably

contain errors due to miscalibrated sensors, communication

problems, or equipment failures, among many other possible

causes. These errors, unless properly identified and corrected in

a consistent fashion, will infect and retard all downstream

efforts to use the data either for analysis or to drive

applications.

When these data quality issues arise, it can be challenging to

distinguish between anomalous grid behavior and anomalous

data. Customized data checking and validation system and

detection of bad data in every data stream are at present not

scalable nor automated. Bad data are often found after they

have been ingested and stored, when analysis is attempted.

Machine learning methods can be used to monitor the quality

of data and detect anomalous readings, do online calibration in

coordination with otherwise redundant system information, and

compare measurements across different time scales to improve

the accuracy and value of any downstream system.

The systems that might use distributed analytics may be very

complex. Some sort of monitoring, verification, and evaluation

system must be in place to ensure the distributed processing

across the grid is performing effectively and not experiencing

issues that could be related to cybersecurity, communication, or

data system degradation. This will require additional data,

communication, and monitoring on the analytic systems

themselves. One example is satellite lock in GPS-based time

synchronization, as an additional data stream from the

measurement device that indicates the health of the data

themselves.

Key metrics: Reliability, correctness, cost


EGS level: Component state, electrical state

Scope of activity: Develop and demonstrate machine learning

techniques to monitor data quality, improve calibration, and

identify potential system issues using reported data streams.

Conduct collaborative studies among academia, national labs

and industries, making use of co-simulation capabilities to

identify system performance indicators and vectors for

performance degradation though the chain of data and

information. Metrics will be developed for which each data

stream will be investigated.

3 Multi-Modal Multivariate Algorithms

A significant volume of analyses is already being proposed for

the power grid/buildings interface. Analyses such as

consumption, forecast of load, and outages at present often rely

on single data sources; as an example, a smart meter’s on/off

status can be used to diagnose an outage location. Within the

existing analytics platforms, where techniques such as machine

learning are already implemented, there are numerous instances

of siloed data sources and techniques. The analytics developed

are often specific to the grid and sensor architecture, meaning

analytics do not thrive upon the wealth of data available and are

dependent on single-source accuracy. Research is required to

implement both multimodal and multivariate techniques for

present and future grid data sources. There is a need for

development of advanced analytics techniques combined with

motivating applications as a core foundational focus to realize

the objectives of the sensing and measurement strategy. The

first step is actually ingesting or accessing the data from many

legacy applications and multimodal sources, as well as new

sensors and systems. Data from many networks, including the

T&D electrical network, weather networks, communication

network traffic, forecasting systems, Twitter feeds, asset

management, and many others may require fusing. All these

data need to be gathered by or accessible to one or more

advanced analytic processing applications in a consistent, cost-

effective manner.

Key metrics: Latency, reliability, correctness, cost


EGS level: Component state, electrical state

Scope of activity: Development and demonstration of

multimodal, multivariate machine learning techniques for real-

time and predictive analysis of a wide range of grid conditions

as presented in the use cases.

88

89

Advanced Data Analytics Techniques and

Applications

Problem: There is a need for development of advanced

analytics techniques combined with motivating applications as

a core foundational requirement to realize the objectives of the

sensing and measurement strategy.

Definition of metrics

• Local—Analytics performed at, on, and for a single

sensor/asset location without input from other devices.

Data do not leave the device in question.

• Distributed—Analytics and decisions are made across

sensors/assets distributed in space and time. Data are

shared across the network but not consolidated to a single

location.

• Centralized—Analytics and decision making

(computations) are performed at a single central location.

Data must be moved to this location.

• Retrospective (historical)—Computations are performed

on data stored from a time in the past.

• Real-time (present)—Computations are performed within

some window of time of the present moment. This time

period often has the most stringent time budget

requirements.

• Predictive (future)—Refers to all analytics whose results

are estimates of future values. The time budget for this

computation is dependent on how far into the future the

prediction is made.

Metrics for data management and analytics

Level Time Retrospective (past) Real-time (present) Predictive (future)

Local Data acquisition latency: minutes

Computational budget: moderate

Solution time: minutes

Nodes: 1

Scalability: 100K nodes

Precision: single node/device

Accuracy: <5% root mean square error (RMSE)

Data acquisition latency: 0 to us

Computational budget: low

Solution time: <microseconds

Nodes: 1

Scalability: 1


Accuracy: <5% RMSE

Data acquisition latency: ms


Solution time: seconds

Nodes: 1

Scalability: 1


Accuracy: <10% RMSE

Distributed Data acquisition latency: minutes



Nodes: 100K

Scalability: 1M

Precision: high (local decision making)

Accuracy: <5% RMSE

Data acquisition latency: microseconds

Computational budget: low

Solution time: <microseconds

Nodes: 100K

Scalability: 100K


Accuracy: <5% RMSE

Data acquisition latency: ms

Computational budget: high


Nodes: 100K

Scalability: 100K


Accuracy: <5% RMSE

Centralized Data Acquisition Latency: minutes



Nodes: 1M

Scalability: 10M

Precision: single device

Accuracy: <5% RMSE



Solution time: <us

Nodes: 500K

Scalability: 500K

Precision: regional

Accuracy: <5% RMSE




Nodes: 500K

Scalability: 500K

Precision: regional

Accuracy: <10% RMSE

90

Weather Sensing Data—R&D Thrusts

This focus area includes research thrusts for advanced

modeling using weather sensing data with quantitative

metrics where appropriate. There are additional research

thrusts related to innovative devices for weather sensors,

which appeared earlier under the DEVICES section.

Key measurement parameters: Wind speed, wind

direction, temperature, humidity, soil moisture, water

turbulence (offshore wind), irradiance (GHI, DNI, and

diffuse), spectral components, cloud motion, barometric

pressure, precipitation, lightning, icing, renewable power

generation

EGS levels: Topological state, component state, building

state, ambient state, convergent networks

1 Harnessing Existing Disparate Weather Monitoring

Resources and Enabling Their Optimal Use

There is a great amount of weather monitoring and

measurement resources in the nation, including ground-

mounted sensors, weather stations, mesonets, remote-

sensing, and satellite measurements. These resources (such

as next-generation GOES satellites) are capable of

providing visibility, at high temporal and spatial resolution,

into various weather parameters for renewable energy and

electricity demand forecasts. Data from weather radar that

provide precipitation detection capabilities can be adapted

to quantify real-time impacts on PV plant statuses and

production estimates. Data quality assurance and

standardization of formats are important. Although there

have been efforts to standardize meteorological data

reporting to a certain extent, such efforts need to extend to

the resource forecasting data used by the energy industry.

Currently, every utility and ISO has its own data formats to

ingest the weather and renewable forecast information for

various applications. Standardization in terms of data

reporting practices (i.e., measured parameters and their

metadata) and forecast integration will be key areas for

widespread adoption of weather data. Standards and best

practices for calibration of weather sensing devices are also

needed.

Key metrics: Cost of data acquisition, data availability and

redundancy, latency, spatial (<1 km, behind the meter) and

temporal (in seconds) coverage, measurement quality

(>99% accuracy), high-resolution high-speed weather data

curation (>100 terabytes per day)

Attributes: Improving energy forecasting, reducing data

acquisition costs, enabling entry of nondomain (power)

experts for grid modernization, workforce training, high-

quality data, decision aid tools that enable seamless

weather data integration, and cyber security

Scope of activity: (1) Create a consortium of key personnel

responsible for measurement, generation, communication,

assimilation, and end-use. (2) Facilitate public and private

partnerships, standards development. (3) Develop

comprehensive documentation of disparate weather sensing

resources. (4) Work with utilities and ISOs to understand

format variations and their rationales. (5) Develop and

enforce industry best practices for weather monitoring

sensor deployment, maintenance and operation. (6)

Consider Pareto front of weather sensing infrastructure cost

vs. system performance (e.g., reliability, flexibility,

observability).

2 Advanced Modeling of Resource Observability and

Forecasting

Advancements in weather forecasting models have

contributed to better forecasting of energy supply and

demand. However, utilities still have limited visibility of

feeder-level or substation-level net loads. This impacts grid

management applications, including bulk system reserve

allocations, distribution system fault detection, and voltage

management. The future grid requires innovative models

that can provide real-time production and forecasts at very

high spatial and temporal resolution for behind-the-meter

renewables and loads. This will necessitate development of

cloud retrieval and of radiative transfer algorithms that

enable real-time processing of very high-resolution satellite

data. Localized forecasts using sky camera technologies

require a significant increase in accuracy and decrease in

latency that can be realized only through innovative

modeling techniques. Additionally, current forecasting

models are not capable of predicting clouds and the

consequent solar radiation with the required accuracy

necessary for future grid applications. Improvements in the

understanding of the physics of cloud development,

sustenance, and dissipation, and innovations in assimilation

of newer measurements, are necessary to transform the

current state of the art. The use of tools such as big data

analytics in addition to typical weather forecasting models,

including numerical weather prediction, will have an

important role to play.

Key metrics: Improvement of forecast accuracy of

renewable generation, ramps, and the consequent net load

at different locations (>30–40% compared with state-of-

the-art practices; probabilistic forecasts: >95% accuracy in

uncertainty coverage and <5% mean bias error); spatial

(<1 km, behind the meter) and temporal (5–15 min. for

real-time dispatch, hourly for day-ahead) coverage

Attributes: Variable renewable integration, net-load

forecasts, advanced T&D market design, lean reserves,

resilience, operational flexibility, optimization of cost and

reliability

Scope of activity: (1) Develop advanced forecasting

models for probabilistic forecasts of load, variable

renewables, and net-load power and ramps. (2) Work with

industry to evaluate the value proposition and

recommended best practices for advanced forecast

integration. (3) Validate satellite data based on ground-

mounted sensors and the resulting forecasting models.

3 Weather-Dependent High-Impact Event Modeling

Weather data is a key piece in developing decision support

tools for severe resilience events. More than 70% of

outages are correlated with weather events. Even without

considering very severe weather events—such as high

wind, lightning, storms, forest fires, and floods—shorter

duration (<4 hour) outages can hamper industrial activity

and cause economic losses. On average, the US economy

loses $104–164 billion a year to outages, and this could

increase depending on the frequency of severe events.

Integration of variable renewables that depend on weather

forecasts adds complexity for both (1) predicting the impact

of their variability and uncertainty and (2) their roles and

impacts on system recovery with synergic storage systems.

The impact modeling of severe weather goes beyond the

electrical grid to other interdependent infrastructure such as

gas and transportation.

Research will include ingesting severe weather events into

visualization tools; translating the weather propagation

models into grid impacts; and overlaying the evolution of

severe weather events on the GIS data for distribution

grids, critical loading facilities, and emergency shelters.

Understanding the distributed solar and DER (storage, fuel

cell) locations will also be valuable for (1) identifying

which locations and associated grid assets and customers

will be affected and (2) developing short-term and long-

term preparedness or preventive strategies, including

strategic system restoration based on real-time sensors.

Key metrics: Flood prediction, short-term forecast

accuracy (>95% reliability or uncertainty coverage of

91

probabilistic forecasts), low latency or rate of forecast

updates (seconds to minutes), accuracy of ramp alerts (<5%

false alarms and risks)

Attributes: Situational awareness for grid operators, tree

trimming management, resilience, flexibility, real-time

decision support (local decisions), reduced curtailment,

reduced number and duration of customer outages, fewer

crew truck rolls, increased value proposition for variable

renewables and their synergic storage/demand response

technologies

Scope of activity: (1) Work with ISOs, utility, weather

scientists, and vendors to develop visualization software

that can integrate live forecast feeds to demand

management system/emergency management system

platforms and relate them to probable grid outages. (2)

Quantify the uncertainty of weather events and their

impacts on preparedness to enable resilience at low cost.

(3) Develop decision support tools that use weather data to

identify severe weather warnings and hotspots in the power

grid or customer outages.

92

A-1

APPENDIX A. DEFINITIONS

Abnormality A condition, state, or quality that is not normal in terms of expected condition or

outcome.

Accuracy More commonly, is a description of systematic errors, a measure of statistical bias,

as these cause a difference between a result and a “true” value. ISO calls this

trueness. Alternatively, ISO defines “accuracy” as describing a combination of both

types of observational error (random and systematic), so high accuracy requires both

high precision and high trueness.

Affordable Inexpensive or reasonably priced and within the financial requirements for

providing a short payback period for benefits.

Automatic

generation control

(AGC)

Automatic generation control enables equipment to automatically adjust generation

in a balancing authority (BA) area from a central location. It provides the function

of maintaining the BA’s interchange schedule plus frequency bias.

Ambient state The state of the current operating surroundings of system operation, usually in

relation to the environment. For power systems, it refers to the external conditions

that affect it, such as weather and operational constraints such as environmental

emissions rules, NERC reliability standards, and a variety of dispatch and market

rules.

Angle resolution The smallest change in the value of an angle that can be reasonably measured by a

measurement device (sensor).

Backhaul In a hierarchical telecommunication network, the backhaul portion of the network

comprises the intermediate links between the core network, or backbone network,

and the small subnetworks at the edge of the entire hierarchical network.

Behind the meter A location on the customer/owner side of the electric (kWH) meter. This is opposed

to being on the utility or grid side of the kWH meter.

Building

management

system

A computer-based control system installed in a building that controls and monitors

the building's mechanical and electrical equipment, such as ventilation, lighting,

electrical systems, fire systems, and security systems.

Building state The status of a building’s operating condition as described by measurements, such

as indoor temperature; and operating state of the heating, ventilation, and air-

conditioning, and other parameters, at a particular time.

Bus voltage In a power system, those voltages at the main source, such as a substation or a

connection point along the circuit. It is usually specified for power system power

flow studies. For electronics, it is a voltage that supplies all the circuits of an

electronics system.

Calibration The comparison and verification of measurement values delivered by

instrumentation under test with those of a calibration standard of known accuracy.

Cybersecurity for

Energy Delivery

Systems (CEDS)

Provision of security measures (in hardware/software) to protect against

cyberattacks such as software hacking by intrusion by outsiders.

Centralized vs.

decentralized

These are two typical and diverse system structures. In a centralized structure, a

central unit gathers all the information and exercises control over the lower-level

components of the system directly. In a decentralized structure, complex behavior

emerges through the lower-level components operating on local information without

the control of a central unit.

https://en.wikipedia.org/wiki/Telecommunications_network

https://en.wikipedia.org/wiki/Core_network

https://en.wikipedia.org/wiki/Backbone_network

A-2

Cooperative (co-op) An organization such as an electric utility provider that is jointly run and owned by

its members.

Co-simulation Different subsystems that form a coupled problem are modeled and simulated in a

distributed manner. In electric power systems, co-simulation is usually performed

on electric power grids and communication networks.

Communication

architecture

The hierarchical structural design of communication systems/networks. It refers to

the topology and configuration of the communication hardware and software and

operating characteristics.

Component

network state

Status of data communication services of a communication network component like

a router or switch.

Conventional

generation

Electric generation from large power plants that have fossil fuels, nuclear power, or

natural gas as their source of energy, in contrast to renewable generation that has

wind, solar or hydro as its energy sources.

Convergent

network state

State of data communication services within a single network. Network convergence

is primarily driven by the development of technology and demand.

Cyber-physical

system (CPS)

A system composed of physical components controlled by computer-based

algorithms. The tight conjoining of and coordination between computational and

physical resources.

Cycle (time) A signal is periodic if it completes a pattern (such as a sinewave) within a

measurable time frame (period) and repeats that pattern over identical subsequent

periods. The completion of a full pattern within a time period is called a cycle.

Cycling A signal is cycling when it repeats a pattern within a measurable time frame.

Data access Software and data management activities related to processing, storing, retrieving,

or acting on data gathered in a database or other repository or delivered to one of

these from a data source, such as a sensor or measurement system.

Data analytics A process of inspecting, cleansing, filtering, processing, transforming, and modeling

data with the goal of discovering useful information from the data to draw

conclusions and support decision-making. Also known as data analysis.

Data quality The condition of data quantities with regard to completeness, soundness, and

accuracy.

Data validation The process of ensuring that data are of high quality

Demand response Changes in an end-use customer’s electric power demand/usage from the normal

consumption pattern as a response to incentives. It can be in response to electricity

price signals (i.e., changes in the price of electricity over time), or to incentive

payments offered by the utility designed to induce lower electricity demand/use at

times of high wholesale market use/prices or when system reliability is jeopardized.

Distributed energy

resource (DER)

DERs generate electricity from small-head hydro, wind, or solar power (if

renewable) and fossil fuel (if not). They are typically located near end-use

customers that use them to produce their own electricity or offset their electric

demand. These power sources can be aggregated by third parties to provide power

necessary to meet regular electricity demand/use.

Diffuse horizontal

irradiance

The amount of solar radiation received per unit of area on a surface indirectly from

the sun on a surface that has been scattered (diffuse) in the atmosphere.

Distributed Spread out in various locations, as opposed to being located centrally at one place.

Distributed

generation

Electrical generation and storage performed by a variety of small (compared with

central generation plants) grid-connected devices.

https://en.wikipedia.org/wiki/Data

https://en.wikipedia.org/wiki/Database

https://en.wikipedia.org/wiki/Information_repository

https://en.wikipedia.org/wiki/Data_cleansing

https://en.wikipedia.org/wiki/Data_transformation

https://en.wikipedia.org/wiki/Data_modeling



A-3

Direct normal

irradiance

The amount of solar radiation received per unit of area on a surface directly from the

sun

Dynamic A process or system characterized by constant change, activity, or progress.

Dynamic range The range of dynamic operation, such as transmission line dynamic loading, in

terms of its minimum to maximum value.

Electric topology The configuration of the electric power network in terms of interconnections of the

circuits and components.

Electrical state The status of the electric system described by electrical measurements at a location

and time.

Embedded sensor A sensor embedded (integrated) into a microprocessor system for signal acquisition.

Energy

management

system (EMS)

A system of computer-aided tools used by system operators of electric utility grids

to monitor, control, and optimize the performance of the electric power generation

and/or transmission system.

Extended grid state

(EGS)

To address the future needs of the modern grid, the concept of grid state must be

extended to include all aspects of the electrical power state for distribution systems

and elements that address DERs, including those that are not utility owned, such as

energy storage and new electronic loads. The EGS definition includes both utility

and customer assets in the distribution system and connectivity with the

transmission system.

Flexible AC

transmission

system (FACTS)

A hardware/software system, generally power electronics based, used for the

flexible control of power on the transmission system.

Fault An abnormal electric current due to the short-circuiting of the power system, such as

a leaning tree causing a short-circuit (fault) on the distribution system.

Flexible The ability of an entity, such as the power system, to adjust controls, protection, and

so on to respond to changes in operating conditions/states, such as changes in

electric supply/demand and/or from normal to emergency conditions.

Flexible generation Electric power generation with the ability to change its operating condition quickly,

for example, to start and stop on short notice, change its output (ramp) rapidly, or

achieve and maintain a low minimum operating level.

Frequency

monitoring

network (FNET)

device

“FNET” is a low-cost, quickly deployable, GPS-synchronized wide-area frequency

measurement network deployed by the University of Tennessee. Frequency

disturbance recorders (FDRs)—GPS-synchronized single-phase phasor

measurement units (PMUs) installed at ordinary 120 V electrical outlets—are used

in the FNET system to measure local voltage/angle and frequency as well as grid

frequency. Because the voltages at which FDRs are connected are much lower than

those of a typical three-phase PMU, the devices are relatively inexpensive and

simple to install. They can be installed in buildings/homes without all of the

requirements the PMUs need for installation in substations.

Frequency The number of occurrences of a repeating event per unit of time.

Geomagnetically

induced current

(GIC), geomagnetic

disturbance (GMD)

GIC and GMD events, which induce DC voltages and currents on the electric power

system, are caused by solar flares ejected from the surface of the sun.

A-4

Global horizontal

irradiance (GHI)

The amount of short-wave radiation received on the surface horizontal to the ground

and the total of direct normal irradiance, diffuse horizontal irradiance, and ground

reflected radiation.

Grid

Modernization

Initiative

An initiative created by the US Department of Energy (DOE) to create the electrical

power grid of the future.

Grid

Modernization

Laboratory

Consortium

(GMLC)

A strategic partnership established between DOE and its national laboratories to

bring together leading experts, technologies, and resources to collaborate on the goal

of modernizing the nation’s grid.

Grid hardening Strengthening electrical assets to withstand major storm events, which include high

winds, lightning, flooding, and heavy snow and ice.

Grid optimization Selection of interrelated decisions on planning, operating, and controlling power

grid assets that maximize an objective, such as minimizing total cost or maximizing

reliability within allowed engineering, market, and regulatory constraints.

High-resolution

sensor

The resolution of a sensor is the smallest change that it can detect in the quantity

being measured. Thus, high-resolution sensors can detect very fine or extremely

small changes in a measured quantity.

Human interface

device

A device (e.g., keyboard, mouse) by which humans provide input to and output from

a computer-based system. In the industrial design field of human-computer

interaction, it is the space where interactions between humans and machines occur.

Industrial control

systems cyber

emergency

response team

(ICS-CERT)

An organization of the US Department of Homeland Security within the National

Cybersecurity and Communications Integration Center (https://ics-cert.us-

cert.gov/about-us) that operates 24/7 to reduce risks within and across all critical

infrastructure sectors.

Intrusion detection

system/intrusion

prevention

system/unified

threat management

(IDS/IPS/UTM)

IDS is hardware/software that monitors a network or systems for malicious or policy

violation activity such as hacking. IPS is a threat prevention system that monitors

and examines network traffic to detect and prevent vulnerability to intrusion attacks.

UTM is a set of security appliances that combine firewall, antivirus and intrusion

detection/prevention capabilities into one platform.

Internet of things

(IoT)

A network of physical devices—such as computers, phones, home appliances,

vehicles, and other devices—embedded with electronics, software, sensors,

actuators, and network connectivity that enables these devices to connect and

exchange data.

Interoperability The ability of a product or system of different manufacturers, whose interfaces are

completely understood, to work with other products or systems without any

restrictions.

Investor-owned

utility (IOU)

A privately owned/operated electric utility rather than one operated by the

government or a cooperative.

Irradiance The radiant flux or density of radiation (power in W/m2) received by a surface per

unit of area. Solar irradiance is the power per unit area received from the sun.

https://en.wikipedia.org/wiki/Industrial_design

https://en.wikipedia.org/wiki/Human%E2%80%93computer_interaction

https://en.wikipedia.org/wiki/Human%E2%80%93computer_interaction

https://ics-cert.us-cert.gov/about-us

https://ics-cert.us-cert.gov/about-us

https://en.wikipedia.org/wiki/Embedded_system

https://en.wikipedia.org/wiki/Electronics

https://en.wikipedia.org/wiki/Software

https://en.wikipedia.org/wiki/Sensor

https://en.wikipedia.org/wiki/Actuator

https://en.wikipedia.org/wiki/Internet_access


A-5

Latency The delay in data transfer—for example, a communication delay following an

instruction for data transfer. A time interval between the stimulation and response,

or, from a more general point of view, a time delay between the cause and the effect

of some physical change in the system being observed.

LIDAR (light

detection and

ranging)

A light and radar technology that measures distance by illuminating a target with a

laser light.

Luminance The intensity of light that passes through, is emitted by, or is reflected from a

particular area in a given direction.

Machine learning A subfield of computer science, which evolved from the study of pattern recognition

and artificial intelligence, that enables computers to learn or act without being

explicitly programmed.

Measurement Physical quantities or parameters detected by sensors associated with physical

action, events, or phenomena.

Multi-Year

Program Plan

The Grid Modernization Multi-Year Program Plan developed by DOE

Narrowband

Internet of Things

(NB-IoT)

NB-IoT is a low-power wide area network (LPWA) standard that improves power

consumption of user devices, system capacity, spectrum efficiency, and deep

coverage to enable a wide range of new IoT devices and services.

Network function

virtualization

A network architecture concept that uses information technologies to virtualize, or

create in software, entire classes of network node functions, which act as building

blocks that connect, or chain together, to create communication services. It is a way

to reduce cost and accelerate service deployment for network operators by

decoupling functions like firewalls or encryption from dedicated hardware and

moving them to virtual servers.

Network (WAN,

LAN, HAN, NAN)

A network is an interconnection of various devices such as sensors, meters, and

switches to communicate and share data via wired or wireless communication.

Networks can be differentiated by their reach, i.e., geographical area. A wide area

network (WAN) connects regional and national networks together. A local area

network (LAN) interconnects various devices within a limited area such as a

residence, school, laboratory, university campus or office building. A home area

network (HAN) is the connection of network-enabled devices in a residence. A

near-me area network (NAN) focuses on wireless communication among devices in

close proximity.

Networking and

Internet

Technology

Research and

Development

Program (NITRD)

“The Networking and Information Technology Research and Development

(NITRD) Program is the Nation’s primary source of federally funded work on

advanced information technologies (IT) in computing, networking, and software.

The multiagency NITRD Program seeks to provide the research and development

(R&D) foundations for assuring continued US technological leadership and meeting

the needs of the Federal Government for advanced information technologies. The

NITRD Program also seeks to accelerate development and deployment of advanced

information technologies in order to maintain world leadership in science and

engineering, enhance national defense and national and homeland security, improve

U.S. productivity and economic competitiveness, protect the environment, and

improve the health, education, and quality of life of all Americans. Reference:

https://www.nitrd.gov/about/index.aspx.

https://en.wikipedia.org/wiki/Computer_science

https://en.wikipedia.org/wiki/Computer

https://www.britannica.com/science/metrology

https://www.britannica.com/science/metrology

A-6

Node/nodal “Node” can refer to a device or a location within a communication network, data

system or electric power system. In a communication network, a node/nodal is

either a redistribution point or a communication endpoint. In a data system, a node

may be either a data communication equipment or data terminal equipment. If the

network is a distributed system, the nodes are clients, servers, or peers. In an electric

power system, a node can be any point on a circuit where two or more circuits or

elements meet and connect.

Operational

technology (OT)

Hardware/software that detects or causes changes in response to the monitoring

and/or control of physical devices, processes and events in the enterprise system.

Optical transducer Electronic detector for measuring physical values on the power system by

converting light, or a change in light, into an electronic signal. An optical transducer

can realize high signal fidelity by intensity modulation using a noncoherent light

source that passes through fiber optic cables without being distorted by any

saturation effects.

Phasor A complex number representation of a power system voltage or current waveform.

It represents a sinusoidal function of amplitude (A), angular frequency (ω), and

angle/phase (θ). It is related to a more general concept called analytic representation,

which decomposes a sinusoid into the product of a complex constant and a factor

that encapsulates the frequency and time dependence of the signal.

Plug and play Describes devices that recognize, are recognized by, and work with a network or

computer system as soon as they are connected. With this capability, the user does

not have to manually install drivers for the device or even tell the computer that a

new device has been added. Instead, the network or computer system automatically

recognizes the device, loads new drivers for the hardware if needed, and begins to

work with the newly connected device.

Phasor

measurement unit

(PMU)

A device that produces time-synchronized phasors and frequency and rate-of-

change-of-frequency (ROCOF) estimates from instantaneous voltage and/or current

signals of the power system. The measurements are time-synchronized using a

highly accurate time signal, such as GPS signals. Note that the same device may not

be a dedicated PMU and may perform other functions and have another functional

name (e.g., the device may also record power system waveforms and be called a

digital fault recorder or may also perform protection functions, such as those of a

relay.)

Plane of array

(POA)

The surface of the photovoltaic array. It is important for determining its orientation

with respect to the sun to maximize energy production.

Power flow The flow of electric power on power systems. Also can refer to the solution of

voltages and electric power flows in a power system software solution or simulation.

Power quality The quality of electric power provided for end-use consumers and their devices. It

can refer to the level of harmonics, flicker, and other quality characteristics of

electric power provided to end users that can affect the operation of various electric

loads and appliances.

Power system The electricity system, consisting of generation resources and transmission facilities,

under the management or supervision of an independent system operator, reliability

transmission operator, or transmission system operator or owner to meet electric

load and/or interchange energy commitments.

PQ node A measurement device that can be placed near an electric consumer/load to measure

the quality of power provided by the utility system.

A-7

Precision The accuracy of a measurement system. It is related to reproducibility and

repeatability. It is the degree to which repeated measurements by the measurement

system, under unchanged conditions, show the same results.

Quality of service

(QoS)

QoS for networks includes transmission rates, error rates, and other network

characteristics that can be measured, improved, and to some extent guaranteed in

advance. QoS is an industry-wide set of standards and mechanisms for ensuring

high-quality network performance for critical applications.

Ramping Can be related to the ramping either up or down of generation or load. Load ramp is

a sudden change in system net load due to changes in energy consumption (e.g.,

evening load ramp up) and/or renewable energy generation (e.g., evening solar ramp

down) or a conventional generation outage (i.e., causing generation-load

imbalance). Load ramping needs are typically met by corresponding changes in

electric power generators or demand response, which either increase or decrease

their power output or consumption. The amount of ramping provided by a resource

depends on its ramp-up or ramp-down rates (defined in terms of MW/min).

Real time Relates to applications in which the hardware and/or software system must respond

as rapidly as required by the operator/user or as necessitated by the process being

controlled within the physical constraints of the operating system.

Recloser A self-controlled protection device for automatically interrupting and reclosing an

AC circuit on an electric power system, with a predetermined sequence of trips

(opening) and reclosing followed by resetting, hold-closed, or lockout operation.

Reclosers interrupt temporary faults on an electric circuit, such as a tree

momentarily touching an energized line, and lock out the circuit when a fault is a

permanent one, such as a downed line.

Registered/

unregistered

Registered memory modules have a register between the dynamic random-access

memory (DRAM) modules and the system's memory controller. They place less

electrical load on the memory controller and allow single systems to remain stable

with more memory modules. Compared with registered memory, conventional

memory is referred to as unregistered memory.

Reliable The ability of the electric bulk-power system to withstand sudden disturbances, such

as faults (electric short circuits), or the unanticipated loss of system elements, (i.e.,

generator or transmission line trips), from credible contingencies and still provide a

high level of quality of electric power service to end-users.

Renewable

generation

The process of generating electric power from renewable energy sources, which are

those that are naturally replenished, such as with wind or solar energy.

Requirement The singular documented physical or functional need that the electric power system

generation, transmission, and/or distribution system aims to satisfy.

Resilience/resilient The ability of the electric power system or its components to adapt to changing

system conditions and withstand and rapidly recover from a disrupting event.

Responsive load Electric end-use loads that can respond to a utility signal such as price to provide

reduced load demand, for example, during emergency operation. This type of load

can also be used to provide frequency/voltage regulation and spinning reserve.

Restoration The state of the power system operating state when a stable operating point with

partial or total blackout is reached and the process of reconnecting all loads is

started. Full restoration is achieved when all loads have been reconnected and the

system either enters the alert or the normal operating state.

https://en.wikipedia.org/wiki/Result

A-8

Root mean squared

(RMS)

A mathematical process used to determine average voltage/current over a period of

time.

Rate of change of

frequency

(ROCOF)

The change in system frequency over a certain time. The unit of measurement is

Hertz per second or Hz/s.

Supervisory control

and data

acquisition

(SCADA)

The SCADA system is the hardware and software system that provides the remote

control and telemetry used to monitor and control the transmission/distribution

system of the electric power system.

Scalability The ability of a system to scale up by using additional or new generations of

components.

Scalar A physical quantity, which has magnitude and no other characteristics, that can be

described by a single element of a number field such as a real number, often

accompanied by units of measurement. In contrast, vectors and tensors are described

by several numbers that characterize their magnitude, direction, and so on.

Security The ability of the power system to remain operating in a normal state of operation

without serious consequences due to any credible system contingencies.

Sensor/sensor

device

A device, module, or subsystem whose purpose is to detect events or measure

changes in its environment and send the data to other electronics (frequently

computer processors) to produce information.

Sensor optimization Finding a sensor selection and/or allocation with the most cost-effective or highest

achievable performance (e.g., in observability, reliability) under given physical and

budget constraints, by maximizing desired factors and minimizing undesired ones.

Signal-to-noise

ratio (SINR/SNR)

Signal-to-noise ratio

Situational

awareness

Awareness of the operating environment and conditions of the electric power

system. The perception of elements in the environment within a volume of time and

space, the comprehension of their meaning, and the projection of their status in the

near future.

Smart meter An electronic kWH meter that records the consumption of electric energy at the end-

use at intervals of an hour or less and communicates that information via wireless or

wired communication to the utility for monitoring and billing.

SODAR (sonic

detection and

ranging)

A meteorological instrument used as a wind profiler to measure scattering of sound

waves by atmospheric turbulence.

Software-defined

networking

A programmable open-source approach that facilitates network management and

enables programmatically efficient network configuration to improve network

performance and monitoring. It is meant to address the fact that the static

architecture of traditional networks is decentralized and complex, whereas current

networks require more flexibility and easy troubleshooting.

Spectrum

optimization

Optimizing the use of the radio frequency spectrum to promote efficient utilization

and avoid and solve interference. Joint coordination of the transmit spectrum, i.e.,

transmission powers over all frequency carriers, of the interfering users so that the

spectral efficiency is improved.

A-9

Spectrum

utilization

The amount of information (measured in bits) being carried by a frequency

spectrum. An appropriate theoretical measure for spectrum utilization is the average

bits/m2 or bits/Hz or bits/s. The maximum achievable rate of information per unit of

spectrum depends on many factors, ranging from the physical propagation

conditions to the state of technology and system design.

Stability The ability of an electric power system, for a given initial operating condition, to

maintain or regain a state of operating equilibrium after being subjected to a

physical disturbance.

Static synchronous

compensator

(STATCOM)

A regulating device for AC transmission lines/systems that can produce (source) or

absorb (sink) reactive power depending on the transmission system need.

State estimation The act of estimating the state of the network from the redundant telemetry

measurements.

Storage The capture of energy produced at one time and stored for use later.

Sustainable An energy system that serves the needs of the present without depleting and

compromising the ability of future generations to meet their future energy needs.

System protection A branch of electrical power engineering that deals with the protection of electrical

power systems and their assets. Protection equipment includes relays, circuit

breakers, reclosers, fuses, and other devices that protect the system from faults as

well as provide the isolation of faulted parts from the rest of the electrical network.

The objective of system protection is to keep the power system reliable, secure, and

stable by isolating only those components that are faulted, while leaving as much of

the network as possible still in operation.

Thermal generation The process of generating electricity from heat produced from the combustion of

fossil fuels or natural heat from geothermal activity. There are four thermal energy

fuels: coal, natural gas, wood waste, and geo-thermal sources.

Total harmonic

distortion (THD)

The sum of the all harmonic components in the system such as the total for current

or voltage harmonics.

Thermocouple An electrical device consisting of two dissimilar electrical conductors forming

electrical junctions at differing temperatures. A thermocouple produces a

temperature-dependent voltage as a result of the thermoelectric effect, and this

voltage can be interpreted to measure temperature.

Time

synchronization

Maintaining accurate time values on multiple devices located at some distance apart

from each other. Time synchronization is realized by referring to a common and

accurate time source, or multiple time sources with a small enough time differential

to meet the synchronization requirement. In power system measurements, time-

synchronized sensors receive time signals from a reliable and accurate time source,

such as GPS, that can provide time traceable to a timing system, such as coordinated

universal time or UTC, with sufficient accuracy to keep the measurement

timestamps within the required limits.

Time stamp A sequence of characters or encoded time information identifying when a certain

event occurred, usually giving date and time of day but sometimes accurate to a

small fraction of a second. The time stamp of sensor output represents the

measured/recorded signal at the time it was applied to the sensor input.

https://en.wikipedia.org/wiki/Power_engineering

https://en.wikipedia.org/wiki/Fault_(power_engineering)

https://en.wikipedia.org/wiki/Grid_(electricity)

https://en.wikipedia.org/wiki/Electrical_conductor

https://en.wikipedia.org/wiki/Electrical_junction

https://en.wikipedia.org/wiki/Temperature

https://en.wikipedia.org/wiki/Voltage

https://en.wikipedia.org/wiki/Thermoelectric_effect

A-10

Transducer A device that converts energy from one form to another. Transducers are often

employed in measurement, control, and power systems to convert electrical signals

to and from other physical quantities (e.g., energy, force, torque, light, motion,

position). In the case of power systems, a transducer converts high-voltage

parameters on the transmission or distribution system into low-voltage parameters

that are safe for sensors, measurement systems, and personnel.

Transient dynamics Natural response of a dynamic system when it changes from one equilibrium state to

another. Transient dynamics in electric power systems are usually caused by a major

disturbance, such as a generation trip, load shedding, shunt capacitor switching, or

short circuit. Transient dynamics usually include oscillations in the power system

frequency and electromechanical wave propagation.

Technology

readiness level

(TRL)

The TRL is a method of estimating the technology maturity of equipment for real-

world uses, such as critical technology elements of a program during the acquisition

process.

Unmanned aerial

vehicle (UAV)

A UAV, commonly known as a drone, is a small aircraft that is operated remotely

by a human pilot.

Use case An example case to illustrate how the method or approach works or might work.

Variable

renewables

Resources such as wind and solar power that have variability and uncertainty in

their electric energy provision due to variations in environmental conditions.

Visibility The degree to which the operating states and asset conditions of a system are visible

or observable to the system operator or engineer. The quality or fact of being visible

or degree to which something is visible.

B-1

APPENDIX B. CYBER-PHYSICAL SECURITY

The power system already uses multiple layers of sensors (e.g., electrical, mechanical, chemical),

transducers (potential and current transformers), and actuators (e.g., breakers, capacitor banks, voltage

regulators). The sensors detect, while actuators control the power flow, voltage level, and power quality

from generation through the transmission/distribution system to end loads. Additionally, the roadmap

document identifies a large variety of new sensor solutions through the work of national

laboratory/industry working groups.

These sensors already must balance three non-orthogonal needs:

1. Application requirements: Requirements dictated by optimal resolution and accuracy needs to

support decision-making frameworks at utilities.

2. Integration requirements: These are dictated by utility operational frameworks in procedures

governing the deployment of new sensors into existing infrastructure with the least disruption to

reliability, and their integration and interoperability with existing sensing and control infrastructures.

3. Cost requirements: Adoption of new technologies at cost-effective scale, particularly in legacy

electric grid assets, drives the sensor cost requirements which vary at various levels of the grid

infrastructure (e.g., monitoring transmission assets vs. distribution assets vs. end use).

Because of the increased importance of cybersecurity in power networks, sensors also need to add cyber-

physical security awareness and support to their list of requirements to enable them to detect and mitigate

complex cyber threats in the power grid.

Figure B.1 shows how the 5-tier control system architecture can be used to describe the interaction

between the operational technology (OT) and information technology (IT) components of energy systems.

Often, tiers 0–2 constitute the OT system and tiers 3–5 constitute the IT system. Although the OT system

elements are frequently secured via an assortment of authentication, certificates and keys, and secure

provisioning tools and practices, end users deal with the practical details of the OT-IT differentiation. As

an example, consider Figure B.2, which illustrates ExxonMobil’s approach to OT-IT partitioning layered

over the aforementioned 5-tier design structure. It is immediately apparent that the two facility operations

have different areas of control but share the common value that cybersecurity of the information flow

from layers 0 to 4, in the case of Figure B.2, must be maintained. New practices of bridging IT and OT

networks or connecting OT devices to the internet have exposed the power system to new attack vectors.

Note specifically that cybersecurity network designs found in best practice documents such as NIST 800,

and illustrated in Figures B.1 and B.2, have an overwhelming reliance on firewall segmentation between

the networking layers. Firewalls are traditionally IT-system focused and are not invulnerable. OT-IT

systems, on the other hand, require a unique set of defenses that accommodate their combined

architecture. While firewalls are generally a good practice, current R&D is emphasizing a different

approach with focused minimization of internet connectivity—even with device upgrades.

There is a need to build cybersecurity measures into the software code for the sensor microcontrollers’

VPN capability, thereby securing a transport tunnel into different layers of the overall framework. In

addition, device authentication for a SCADA/distributed control system—validating the device via

blockchain with the network, but not the actual information (measurement)—is being developed. Placing

additional capabilities into the sensors-at-the-edge adds potentially increased cyber security.

B-2

Figure B.1. Five-tier industrial control system (ICS) architecture.

Figure B.2. ExxonMobil’s system architecture illustrates a clear demarcation between OT and IT.

B-3

Sensors represent both an opportunity and a risk for power system cybersecurity. On the positive side,

they are critical instruments for detecting and mitigating cybersecurity threats to power system

infrastructure. Sensors designed to measure and analyze communication systems are useful for intrusion

detection and intrusion prevention system. These tools can alert IT or OT network operators to adversarial

actions or reconnaissance by hackers. For instance, firewalls can log all traffic from external IP addresses

and warn or block traffic when specific protocols are used. Similarly, data analytics can be used to

compare power system measurements with network communications; for example, a relay may report that

it is closed while the downstream voltage is reading zero, which indicates that it is open. The fact that this

information is inconsistent could indicate that the relay or voltage sensor or both were involved in a

spoofing attack.

Unfortunately, sensors are also vulnerable to cyber attacks, including spoofing, denial of service, and

man-in-the-middle. For example, in the scenario mentioned in the previous paragraph, the potential

transformer might actually be the sensor device providing the manipulated (voltage) data. Often, sensor

communications are simple serial or other unrouteable (layer 2) protocols that are unencrypted until they

reach measurement, analyzing, or processing equipment that has more computing capability (tier 2 in

Figure B.1). At any point in this data gathering or transfer, a cyber attack could occur and corrupt the

data. The sensor measurements could be manipulated or falsified through various transduction

cyberattacks, e.g., ultrasonic proximity sensors26; the low-level sensor measurements, e.g., 0–10 V signal,

could be physically or remotely modified before reaching the measurement and processing equipment; or

the processed or measured data could be changed either at the data concentrator or when reported back to

the industrial control system/plant information system. Each of these attack modes should be considered

for robust, cyber-secure sensor deployment.

Additional hardware-based cybersecurity approaches exist and are in various stages of research.

Resource-constrained devices, such as sensors and sensing applications, are vulnerable to invasive attacks

that are designed to steal keys stored in nonvolatile memory (NVM), and NVM adds cost to these low-

cost devices. A physical unclonable function (PUFs)27 is a novel hardware security mechanism that

provides an alternative key storage mechanism that does not require NVM but rather derives the key from

small analog variations that exist from one copy of a chip to another. Therefore, the key, which is not

stored in digital form anywhere, is derived on the fly as needed and is tamper-evident; i.e., any attempt to

steal the secret destroys the PUF and the ability of the chip to regenerate it. Moreover, a special class of

PUFs, called “strong PUFs,” are able to generate an exponential number of reproducible secret bits that

can be used to harden security protocols further. Moreover, strong PUFs can also reduce area and

energy overheads by reducing the number and type of cryptographic primitives and operations.

There is a bewildering array of cybersecurity threat and attack scenarios that may be associated with the

various layers in a SCADA/digital control system realm. Numerous associations of end users, vendors,

academics, and so on are involved with examining such scenarios. Within DOE, the Cybersecurity for

Energy Delivery Systems (CEDS) program’s Roadmap for Cybersecurity provides a robust intersection

with GMLC sensing and measurement strategy project activities. Individuals interested in examining

CEDS-sponsored projects may wish to visit the CEDS website at https://energy.gov/oe/cybersecurity-

energy-delivery-systems-ceds-fact-sheets,where individual fact sheets are available.

26 https://securityledger.com/2018/01/researchers-warn-physics-based-attacks-sensors/ 27 Wenjie Che, Mitchell Martin, Goutham Pocklassery, Venkata K. Kajuluri, Fareena Saqib, and Jim Plusquellic. “A

privacy-preserving, mutual PUF-based authentication protocol.” Cryptography 1, no. 1 (2016): 3.



C-1

APPENDIX C. SENSOR AND MEASUREMENT TECHNOLOGY

ROADMAP PROCESS

The Grid Modernization and Lab Consortium Sensing and Measurement Strategy project’s Technology

Roadmap document has been developed as a collaboration across the DOE national laboratory system in

close partnership with key partners and stakeholders from industry, academia, and other relevant

government organizations. A list of the participating stakeholder partners can be found in the body of the

report, and a detailed list of participating individuals and their organizations and individuals is found in

Appendix D.

The Sensing and Measurement Roadmap effort has been carried out as an iterative process that

summarizes the current state of the art, outlines existing gaps, and points toward areas of potential need

and opportunity for federal investment to make a significant impact. A graphical illustration of the overall

technology roadmap development effort is presented in Figure C.1. In addition to the work represented in

this graphic, additional efforts continued into calendar year 2018, including review and input by NIST

collaborators, additional revisions by working group leads and the PI/task lead based upon input from

industry stakeholders, and finally refinement by the PI/task lead prior to full publication.

Figure C.1. Technology roadmap development process and timeline.

Detailed Literature Review Including of Existing Technology Landscape

April – June 2016 July – Sep. 2016 Oct. – Dec. 2016 Jan. – March 2017

In-Person Workshop & Revision of Initial Roadmap Slides

Deliverable:Initial Proposed Research

Thrusts to DOE

Internal Team Development of Early Research Thrusts and

Focus Areas

Stakeholder Webinars for

Inputs

Deliverable:Technology Review &

Assessment Document

Project Period 1

Establish Workgroups

August 2017 September 2017 October 2017 December 2017

First Roadmap Draft Development Including

Gap Analysis & Prioritization

Deliverable:First Draft

Roadmap to DOE

Develop and Revise Research Thrusts and Use Cases

Develop a Cross-Cutting S&M Strategy Area Plan

Team Review & Integration of Inputs from Working Groups

Including Gap Analysis

Project Period 2

November 2017

C-2

The first phase of the roadmap process began in September of 2016 with the development of an extended

literature review by the national laboratory team on the topic of the state of the art in sensing and

measurement devices, communication, and data management and analytics technologies as they relate to

the extended grid state of the power system spanning generation, transmission, distribution and end use.28

The result of this effort was a Technology Review and Assessment document, which contains information

on previous roadmaps, technical literature, program documents, and other resources that were used for the

road mapping effort. Based upon some initial identified needs and trends for new sensing and

measurement technologies within the extended power system resulting from this first stage of the effort,

the laboratory team identified an initial set of recommendations for technology gaps and suggested an

initial set of research thrusts and an approach to organizing the Technology Roadmap (which leveraged

the EPRI Transmission and Substation Technology Roadmap format).29 A first draft of the Technology

roadmap without detailed gap analysis or prioritization was presented to stakeholders in a public

workshop held at and hosted by ComEd in February of 2017 to garner initial stakeholder feedback to

inform the path forward. A revised draft of the technology roadmap slides was provided to DOE program

managers for review and input in April of 2017.

The second phase of the technology roadmap process began in August 2017 with the goals of

(1) improving the integration of the extended grid state definition with the technology roadmap;

(2) engaging with stakeholders to refine the proposed research thrusts and perform a detailed gap analysis,

including the development of quantitative metrics; and (3) developing a set of specific, actionable

recommendations for federal initiatives that could advance the objectives of the Grid Modernization

Initiative. The Sensing and Measurement team established several working groups to coordinate

accomplishment of each of these primary objectives (see further details in Appendix D):

1. Crosscutting Sensing and Measurement Support

2. Use Case Refinement and Extended Grid State Integration

3. Harsh Environment Sensors for Flexible Generation

4. Phasor Measurement Units for Grid State and Power Flow

5. Asset Health Monitoring

6. Novel Transducers

7. Sensors for Weather Monitoring and Forecasting

8. End-Use/Buildings Monitoring

9. Distributed Architectures

10. Communications Technology

11. Advanced Analytics

12. Big Data

Each of these working groups operated independently with oversight and coordination by the GMLC

Sensing and Measurement Strategy project PI (Tom Rizy) and roadmapping (Task 2) lead (Paul

Ohodnicki). Each working group developed metrics (quantitative where possible) and a detailed gap

analysis to clarify where additional technologies, tools and techniques are needed to enable better

visibility, understanding, and operating and control capabilities for the complex future modern power

system and to help guide future targeted R&D efforts. The working groups also worked with national

laboratory staff and industry stakeholders to better understand the current state of the art within each

technical area (reflected in the Technology Review and Assessment document) and developed




website at https://gridmod.labworks.org/resources. 29 EPRI Transmission and Substation Technology Roadmap.

C-3

recommendations for a coherent Sensing and Measurement strategy for the Grid Modernization Initiative.

Those elements are reflected in this Roadmap.

D-1

APPENDIX D. WORKING GROUP REPORT SUMMARIES

D.1 CROSSCUTTING NEEDS TO SUPPORT SUCCESS OF THE SENSING AND

MEASUREMENT STRATEGY (LABORATORY LEAD: ZHI LI, ORNL)

D.1.1 Scope of Working Group

A need exists for foundational efforts to support the successful technology development and deployment

of advanced sensing and measurement tools and methodologies throughout the electrical grid

infrastructure. This crosscutting effort will span the various research thrusts and initiatives outlined in

more detail in subsequent sections of the technology roadmap document.

The objective of this crosscutting effort is to raise awareness of the identified issues that are common

across different sensing and measurement areas, create a gateway for stakeholders to efficiently access the

right expertise and resources to address the issues, and provide support, technical or nontechnical,

necessary to facilitate those efforts.

The crosscutting support area was not in the original scope of the Sensing and Measurement Technology

Roadmap. It was initiated based on comments the project team received after the project’s stakeholder

review meeting held in February 2017. Four preliminary crosscutting support initiatives were proposed by

the project team first. Based on the results of reviews and discussions, the crosscutting working group

then expanded them into six initiatives:


2. Data quality and utilization improvement

3. Sensor performance, reliability, resiliency testing, and calibration methodologies

4. Standards and interoperability requirements for deployment of advanced sensors

5. Support for sensing and measurement technology promotion and deployment

6. General crosscutting needs support for industry and utility partners

This working group is to review and critique the six crosscutting initiatives, help the project team develop

a detailed scope and tasks for each initiative, and clarify the structure of the crosscutting sensing and

measurement support area in terms of the organizational framework and interface with existing GMLC

projects.

D.1.2 Working Group Process

The Crosscutting Needs Working Group established a core group of stakeholders spanning the DOE

national laboratory system, academia, federal power operating and research agencies, and vendors. The

following table shows a full list of participants.

D-2

Name Organization Contact information

Scott Averitt Bosch [email protected]

Kang Lee NIST [email protected]

Eugene Song NIST [email protected]

Gordon Mathews NASPI Distribution Task Team/Bonneville Power

Administration

[email protected]

Sudipta Chakraborty Opal-RT [email protected]

Venkat Shastri NASPI Distribution Task Team/University of San Diego [email protected]

Zhi Li ORNL [email protected]

Tom Rizy ORNL [email protected]

Chen ANL [email protected]

The team was first asked to review the four preliminary crosscutting initiatives and comment on whether

they fit in the scope of the roadmap. The working group was also asked to provide ideas on any other

crosscutting initiatives that needed to be included in this focus area. After several rounds of reviews and

analyses, the scope of the four original initiatives was adjusted and clarified, and two new initiatives were

identified according to input from the team members. The team then discussed and refined the task details

of the six initiatives. Some gaps in the crosscutting areas were identified and discussed. Based on the

results of all these discussions, the roadmap input for the crosscutting focus area was updated, and the gap

analysis was conducted in the context of existing GMLC efforts.

Finally, according to the comments received during the second industry review meeting held in Atlanta,

Georgia, in April 2018, the six initiatives were condensed into four. The “data quality and utilization

improvement” initiative (Initiative 2) was removed from this crosscutting focus area, and its contents

were integrated into the Data Analytics and Management focus areas. The sensor testing and standards

initiatives (Initiatives 3 and 4) were combined into one because of the inherent correlations between the

two topics. With these adjustments, four initiatives are recommended as the final output of the work by

this crosscutting working group.

D.1.3 Key Findings and Recommendations

As finalized by the working group, the four recommended crosscutting initiatives are


2. Standards and testing to support improvement of sensor performance, reliability, resiliency, and

interoperability

3. Valuation of sensing and measurement technology

4. General crosscutting needs support for industry and utility partners in technology deployment

Initiatives 1–3 focus on technical issues common across all types of sensing and measurement

technologies covered in the report. Initiative 4 is designed to be a long-standing venue to support industry

and utility partners with general crosscutting needs, even after the activities of the other initiatives have

been closed. The approaches for these initiatives can be summarized as reviewing and documenting

existing knowledge; harmonizing existing requirements and standards; developing new definitions,

standards, and tools/methods; and providing guidance and support. Some of the proposed development

and analysis work can possibly be developed into future stand-alone projects (under GMLC or other









D-3

funding support). Some can be related to or tied in with existing GMLC projects, the results and findings

of which can be readily used to address the crosscutting issues. It is also possible for some of the

proposed crosscutting activities to be merged or coordinated with the existing efforts.

D.1.4 Gap Analysis Summary

Gaps identified by the working group

Relevant

crosscutting

initiative

Approach to address gap

Low awareness level of cyber-physical security of

sensing and measurement systems Initiative 1

Raise awareness of the cyber-physical

security of sensor systems by providing

accurate information, expertise, and

communication channels to the

stakeholders

Lack of comprehensive research dedicated to

cyber-physical issues of sensing and measurement

systems.

Initiative 1

Analyze the security challenges and gaps

in existing sensor infrastructure.

Summarize the cyber-physical

requirements for sensor systems used in

power grid applications

Lack of definition of sensor resiliency and

resiliency testing requirements. Initiative 2

Define standardized definitions,

methodologies, and practices for

benchmarking and testing of sensor

performance, reliability, and resiliency

Nonstandardized testing procedures and

discrepancies in existing testing standards Initiative 2

Harmonize existing testing standards to

eliminate discrepancies

Complications in identifying applicable standards,

interoperability requirements, and testing facilities

for emerging sensor technology

Initiative 2

Maintain an up-to-date understanding of

standards and testing facilities that have

comprehensive capabilities

Develop strategic partnerships with

private- and public-sector partners to

enable access to relevant testing facilities

Insufficient mechanisms to accommodate

emerging sensor technologies in development of

new standards

Initiative 2

Provide technical input into new

standards through active participation and

engagement

Develop sensor-specific working groups

and consortiums for measurement quality

assurance and format standardization for

utility integration

Lack of comprehensive capabilities and

sophisticated tools to conduct valid technology

valuation and regulatory analysis for promotion

emerging sensor technologies

Initiative 3

Identify and categorize relevant

capabilities and tools (e.g., regulatory

analysis, technology valuation) across the

DOE national laboratory system and

maintain up-to-date contact information.

Establish two-way communication

between regulation makers and

stakeholders to help accelerate technology

adoption and deployment

Needs for long-term and continuous efforts to

support the industry and utility partners in some

general crosscutting issues

Initiative 4

Hold regular workshops with industry and

utility partners to maintain a working

knowledge of barriers preventing new

sensing and measurement technology

deployment.

Lessons learned and needs for new

D-4

Gaps identified by the working group

Relevant

crosscutting

initiative

Approach to address gap

expertise and facilities will be

communicated with DOE and GMLC

leadership to identify opportunities where

technical resources within the DOE

system can be leveraged to provide

assistance

For future reference and to show the process of the work done by the working group, details of the gap

analysis based on the original six initiatives (before they were condensed into the four recommended

ones) are provided below:

D.1.4.1 Cyber-Physical Security Awareness and Support

Sensing and measurement systems in the power grid are in the front lines of the battle against cyber-

physical threats. However, awareness of the cyber-physical security issues of the sensing and

measurement systems still, in some sense, remains at qualitative levels. It lacks in-depth understanding of

challenges and technical details that are specific to sensing and measurement devices. The great diversity

of the sensors used in the power grid makes it more difficult to address the issues. Some sensors may

have built-in cyber-physical security measures. However, many sensors operating in the power grid that

contain numerous components may complicate the threats and require more careful considerations and

solutions. Therefore, there is room for a top-down, comprehensive research effort on the cyber-physical

security of the power grid’s sensing and measurement systems.

This crosscutting initiative is to raise awareness of the cyber-physical security of the sensor and

measurement systems in the power grid by developing more technically oriented guidance and reference.

Analysis of the security challenges and gaps for existing sensor infrastructure will be conducted.

Comprehensive cyber-physical requirements for sensor systems used in power grid applications will be

summarized and documented. The initiative will also provide support to stakeholders (mostly the

corresponding researchers, sensing technology developers/vendors, and sensor system users) in improving

the security of existing sensor and measurement infrastructure and developing new sensor projects with

built-in reinforcement of cyber-physical security. It will facilitate the communication channels to bring

the right expertise and resources to the stakeholders to address the cyber-physical vulnerabilities

regarding sensor and measurement applications in the power grid.

GMLC Project 1.4.23, Threat Detection and Response with Data Analytics: This related project is to

develop advanced analytics on operational cyber data to detect complex cyber threats in the power grid.

The outcomes will help power operators differentiate between cyber and non–cyber-caused incidents like

physical attacks or natural hazards. It may provide a tool to support the cyber-physical security needs

discussed in this crosscutting initiative.

D.1.4.2 Data Quality and Utilization Improvement

Data conditioning is an integral part of sensing and measurement processes that are common for every

type of sensing technology. But its importance could be underestimated in many applications, especially

those in harsh environments. Poor data quality and availability could diminish the usability and efficiency

of a sensing and measurement system and even cause the failure of a sensor project. Improved

understanding of the data quality issues and updated knowledge of state-of-the-art data processing

D-5

approaches are necessities for stakeholders and help the promotion of advanced data management

technologies to achieve better utilization of data.

This crosscutting initiative will provide a knowledge set of data quality-related topics and technologies to

help utilities and other industry partners address the challenges on the downstream side of sensing and

measurement caused by poor data quality and availability. It will also provide support to utilities in

adopting advanced data management technologies and improving data utilization. Proposed activities

include understanding data quality issues related to sensor and measurement systems, especially those

working in harsh environments or having restricted requirements for data quality; summarizing the state

of the art of data processing and modeling approaches to improve sensor and measurement system

performance; and hosting workshops or training sessions to promote advanced data management

technologies.

D.1.4.3 Sensor Performance, Reliability, Resiliency Testing, and Calibration Methodologies

Resiliency has become a key factor to be considered in the design of power grid components, including

sensors. As a result, testing of resiliency is drawing more and more attention from the R&D community.

However, big gaps exist in resiliency testing of sensors, from the definition of sensor resiliency to

standardized testing requirements and methodologies, as well as appropriate testing facilities for

evaluating sensor resiliency. In addition, there are discrepancies in existing testing standards and

procedures for testing of sensor performance and reliability.

This crosscutting initiative will target the establishment of standardized methodologies and procedures for

benchmarking and testing functional performance, reliability, and resiliency (in the presence of extreme

natural or man-made events) of sensors before engaging in the full deployment phase. It will also promote

the establishment of a database of testing facilities with comprehensive capabilities in regular

performance and reliability tests as well as resiliency tests. To achieve the goals, standardized testing

requirements for sensor resiliency and methodologies and practices for benchmarking and testing of

sensor performance, reliability, and resiliency will be defined. Harmonization of existing testing standards

to eliminate discrepancies will be conducted. Testing facilities with comprehensive capabilities,

especially in intrusive testing to address resiliency, will be reviewed. Finally, strategic partnerships will

be established with private and public-sector partners to enable access to relevant testing facilities.

GMLC Project 1.2.3, Grid Modernization Laboratory Consortium Testing Network: This related GMLC

project is to close the gap in accessibility to validated models for grid devices and simulation tools and the

corresponding full documentation. It will drive standardization and adoption of best practices related to

device characterization, model validation, and simulation capabilities through facilitated industry

engagement. Some of the findings may help address the testing issues brought up in this crosscutting

initiative.

D.1.4.4 Standards and Interoperability Requirements for Deployment of Advanced Sensors

The types of sensors used in the power grid and their communication setups vary significantly based on

applications. That causes complications in identifying the appropriate standards and interoperability

requirements. The sensing and measurement technologies, and their deployment, should be compliant,

especially for emerging technologies and advanced sensors. On the other hand, the developers of new

standards and interoperability requirements should be aware of the emerging technologies and trends.

Unfortunately, existing tools and/or mechanisms to address both of these issues are insufficient.

This crosscutting initiative will interface with relevant standards organizations to ensure that sensor

development and deployment efforts under the GMLC are consistent with applicable existing and

D-6

emerging standards and requirements. This initiative will also seek to provide technical input into the

development of future and emerging standards and interoperability requirements. An up-to-date

understanding of standards and interoperability requirements specific to sensing and measurement

technology for the electrical grid infrastructure will be maintained. Technical input will be provided for

the development of new standards through active participation and engagement.

Within GMLC, several ongoing projects have been identified as being related to this initiative. The

following are three of these projects with brief descriptions.

• GMLC Project 1.2.2, Interoperability: Will articulate general interoperability requirements, along

with methodologies and tools for simplifying integration and cyber-secure interaction among the

various devices and systems by establishing a strategic vision for interoperability, measuring the state

of interoperability in technical domains, identifying gaps and roadmaps, and ensuring industry

engagement.

• GMLC Project 1.4.1, Standards and Test Procedures for Interconnection and Interoperability: Will

help develop and validate interconnection and interoperability standards for existing and new

electrical generation, storage, and loads that ensures cross technology compatibility, ensures

harmonization of jurisdictional requirements, and ultimately enables high deployment levels without

compromising grid reliability, safety, or security.

• GMLC Project SI-1695, Accelerating Systems Integration Codes and Standards: Will update the

standards identified under the grid performance and reliability topic area, focusing on the distribution

grid. Establishing accelerated development of new interconnection and interoperability requirements

and conformance procedures is the key result for this project.

D.1.4.5 Support for Sensing and Measurement Technology Deployment

Valid and accurate valuation and risk/uncertainty analysis are among the defining tools utilities need to

adopt emerging technologies, including those for sensing and measurement in the power grid. Technology

valuation usually involves extensive analysis and quantitative modeling of technical and economic risks

and benefits. A lack of comprehensive capabilities and sophisticated tools to conduct valid technology

valuation is one of the major barriers for promotion of a new technology. In addition, regulatory activity

may play a leveraging role that could significantly affect technology adoption and deployment and make

the analysis more complicated. Regulatory incentives encourage the adoption of new technologies,

whereas regulatory restrictions may induce extra costs and discourage the adoption.

This crosscutting initiative is to support the clearing of obstacles to the adoption and deployment of

emerging sensing and measurement technologies throughout the modern electrical grid infrastructure,

with an emphasis on regulatory and economic concerns. It will promote the establishment of expertise and

capabilities both internal and external to the DOE national laboratory system to facilitate regulatory

analysis, risk evaluation, and technology valuation for sensor deployment projects. Relevant capabilities

and tools (e.g., regulatory analysis, technology valuation) will be identified and categorized with up-to-

date contact information. Two-way communication between regulation makers and stakeholders will be

established to help resolve misunderstandings and inconsistencies to accelerate technology adoption and

deployment.

Some ongoing projects within GMLC are related to the topic of this initiative, and the findings and results

of those projects might be worth consideration for the proposed work of this initiative. GMLC Project

1.2.4 and 1.4.29 are two examples.

D-7

• GMLC Project 1.2.4, Grid Services and Technologies Valuation Framework: This project is to

address the inconsistencies and lack of transparency across existing valuation methodologies by

developing a comprehensive and transparent framework to value the services and impacts of grid-

related technologies. The valuation framework may be useful to assess “regulated investments,” as

well as investments by private sector entities. The proposed framework might be used for sensing and

measurement technologies.

• GMLC Project 1.4.29, Future Electricity Utility Regulation: This project assists states in addressing

regulatory, ratemaking, financial, business model, and market issues related to grid modernization in

the power sector. It will also help tie utility earnings to consumer value, economic efficiency, and

other public policy goals. Some findings of the project may directly benefit this crosscutting

initiative. The findings could provide insight into issues like how to adapt electric utility regulation

and ratemaking to new technologies and services, assess potential financial impacts on utility

shareholders and customers, consider investments required in infrastructure to enable customer

engagement, and provide incentives to utilities to achieve grid modernization goals.

D.1.4.6 General Crosscutting Needs Support for Industry and Utility Partners

Most of the proposed work of the five crosscutting initiatives can be addressed by one-time or short-term

endeavors. However, after all those activities are accomplished, there still will be a need for long-term

and continuous efforts to support the industry and utility partners in some general crosscutting issues.

Examples may include continuous updating of contact information, expertise lists, technology databases,

and support for recurring events (e.g., workshop). In addition, some new crosscutting needs, such as

expertise matchmaking, may arise on a project-by-project basis. Therefore, having a standing mechanism,

which is missing in the current setup, to support those needs will be necessary and beneficial in the long

run. This initiative is proposed to address those considerations.

This initiative is to provide a long-standing mechanism to support industry and utility partners in general

crosscutting needs. It will promote the establishment of relationships and partnerships among research,

industry, utility. and regulation communities. It provides a standing venue for stakeholders to voice the

challenges they face in the development and deployment of new sensing and measurement technologies

within their systems. Regular workshops will be held with industry and utility partners to maintain a

working knowledge of barriers preventing new sensing and measurement technology deployment.

Lessons learned and needs for new expertise and facilities will be communicated with DOE and GMLC

leadership to identify opportunities where technical resources within the DOE system can be leveraged to

provide assistance.

D.2 HARSH ENVIRONMENT SENSORS FOR FLEXIBLE GENERATION (LABORATORY

LEAD: SYDNI CREDLE, NETL)


Flexible operation of conventional power plants refers to the potential of fossil and nuclear energy to

serve applications other than their traditional baseload operations as part of the grid modernization

strategy. In addition to baseload and spinning reserve, power plants can provide additional services

through flexible operation. Enhanced capabilities for internal monitoring of power generation processes in

real time enables advanced control strategies and designs of conventional plants to reduce any potentially

adverse impacts on the generators, and they encourage more rapid adoption of newer technologies

compatible with energy efficient and flexible operation. This working group will review the current

proposed research thrusts within this focus area of the Roadmap and will develop a clear understanding of

D-8

the current industrial state of the art and quantitative metrics for new sensing and measurement

technology development.


The Harsh Environment working group is composed of key stakeholders from the DOE Office of Fossil

Energy’s NETL and INL. NETL has a long-standing active Sensors and Controls program that specializes

in advanced concepts and technology innovation relevant to harsh environments observed in advanced

energy systems. INL has been engaged for many years in the development of advanced nuclear

instrumentation in support of nuclear fuels and materials test in the Advanced Test Reactor and other

irradiation facilities part of the National Science User Facilities program. The following is a full list of

participants.


Sydni Credle NETL [email protected]

Paul Ohodnicki NETL [email protected]

Benjamin Chorpening NETL [email protected]

Pattrick Calderoni INL [email protected]


The gap analysis summary table in Section D.2.5 presents a summary of the key findings from the

working group, including gaps identified by the working group, approaches to address the gaps, and key

metrics that formulate the basis for evaluation. Key findings include the following:

1. Survivability and durability are of critical importance for harsh environment sensing. Federal

research is needed in advanced materials development, packaging methodologies, and advanced

manufacturing processes to enable robust, reliable, and durable performance within challenging

operational environments that may feature high-temperature (700–1800°C), high-pressure (up to

~107Pa), erosive, and/or corrosive environments and radiation exposure.

2. The ability to co-locate sensing elements with periphery electronics, such as capacitors, resistors,

signal conditioning amplifiers, transmitters, and so on, in close proximity to the harsh service

environments is very advantageous. Federal research in the area of high-temperature and

radiation-hardened electronics is needed to reduce sensor node interfaces, lower system

complexity, improve deployment functionality, and realize overall improvement of operational

measurements.

3. Multipoint, distributed measurements allow for higher-fidelity monitoring capability that produces

broader insight into the status as well as the condition of power generation assets than stand-alone

single-point measurements can supply. Federal research related to implementation of robust

sensing networks and/or arrays with multiple sensor nodes—including data fusion techniques that

combine, filter, and process numerous data streams under high-temperature, challenging thermal

and mechanical loads, radiation exposure, electrical noise, and other parameter excursions—is

essential to the future viability of flexible power generation. Additionally, the development of data

standards and communications protocols within the context of power generation allows for optimized

implementation of distributed sensor networks.


D-9

4. Sensor nodes for harsh environments have a wide variety of constraints that may prevent sustained

power supply to them. In many cases, nodes are remotely located; have limited accessibility; and

require long durations between component lifetimes, maintenance intervals, or specified replacement

periods. Federal research is needed to investigate novel approaches, such as energy harvesting and

other techniques (e.g., wireless power transfer), to powering sensors for continuous operation.

5. Federal research is needed to advance the current state of the art in diagnostic techniques that

encompass the ruggedization of laboratory techniques as well as advanced tools and devices that

can effectively evaluate sensor systems while operating under harsh environment conditions.

D.2.4 Proposed Research Thrusts and Prioritization

Based upon the results of the working group efforts and the associated gap analysis (shown in the table

below), a total of two different research thrusts have been recommended for prioritization. A detailed

description of the research thrusts is presented in the context of all research thrusts for the GMLC

initiative.

The research thrusts recommended are

1. Harsh environment sensing for real-time monitoring (recommended)

2. Advanced electromagnetic diagnostic techniques (recommended)


Gaps identified

by working

group

Relevant research thrust

or thrusts Approach to address gap Key metrics to be addressed

Advanced

materials

development for

sensing elements

Research thrust 1: Harsh

environment sensing for

real-time monitoring

Propose advanced

materials science and

engineering techniques to

develop novel sensing

materials capable of

deployment in harsh

environments

Component-specific performance,

high-temperature compatibility,

stability (under neutron and other

ionizing radiation), and cost

Robust

packaging

technologies to

ensure reliable,

durable

performance




Develop new sensor

packaging materials

capable of withstanding

high-temperature, high-

pressure environments

Component-specific performance,

cost, maximum temperature,

thermal properties (shock resistance,

expansion), compatibility with

sensor materials, low activation in

radiation environment, mechanical

durability during installation or

incidental contact during plant

maintenance

High-temperature

electronics




Research thrust 2:

Advanced

electromagnetic

diagnostic techniques

Develop electronic devices

and circuits capable of

high-temperature operation

while maintaining low

costs

Performance (including radiation

hardness) and cost

D-10

Gaps identified

by working

group


or thrusts Approach to address gap Key metrics to be addressed

Advanced

manufacturing




Development of novel

manufacturing and

fabrication processes that

enable novel concepts,

such as embedded sensing

and other novel multi-

functional designs

Component-specific performance

Multi-point,

Distributed

sensing




Research thrust 2:

Advanced

electromagnetic


Development of multipoint

techniques that enable

distributed measurements

with optimal spatial

resolution for power

generation components

while maintaining low

costs

Number of sensor nodes, spatial

resolution, performance (including

degradation under irradiation), and

cost

Realizing power

requirements for

sensor nodes




Develop novel approaches,

including advanced

methods such as energy

harvesting, to satisfy power

requirements in sensor

nodes

Sensor node energy requirements

and energy availability from

operational environment

Standardized data

and

communication

protocols




More clearly communicate

the challenges associated

with using new sensor data

as barriers to deployment

and implementation

Standards efficacy, awareness by

standards organizations

Data fusion

technologies




Research thrust 2:

Advanced

electromagnetic


Apply advanced data

fusion methodologies to

sensing in harsh

environments to support

distributed, multipoint

sensing

Measurement accuracy, spatial

resolution, and reduced interference

effects

Diagnostic

techniques

Research thrust 2:

Advanced

electromagnetic


Development of advanced

tools that can effectively

evaluate sensor systems

while operating in harsh

environments.

Ruggedization of

laboratory techniques to

make them applicable in

field service and/or

commercial applications

Measurement accuracy, reliability,

performance (including degradation

under irradiation), and cost

D.3 ASSET HEALTH MONITORING (LABORATORY LEAD: PAUL OHODNICKI, NETL)


Asset monitoring for determining the heath condition of various items of equipment in the power system

can potentially be applied to all assets within the electrical power system, including generators, energy

D-11

storage, loads, lines, and power conditioning components. The goal is to determine if the asset is nearing

the time for maintenance, nearing failure, or nearing end of life. This working group will review the

current proposed research thrusts within this focus area of the Sensing and Measurement Technology

Roadmap and will develop a clear understanding of the current industrial state of the art and quantitative

metrics for new sensing and measurement technology development.


The Asset Health Monitoring working group established a core group of stakeholders spanning the DOE

national laboratory system, academia, utilities, and vendors. The following is a full list of participants.


Paul Ohodnicki NETL [email protected]

Alireza Shahsavari NASPI Distribution Task Team/University CA–

Riverside

[email protected]

Gordon Mathews NASPI Distribution Task Team/Bonneville Power

Administration

[email protected]

Jim Glass Electric Power Board of Chattanooga [email protected]

Lilian Bruce Electric Power Board of Chattanooga [email protected]

Stephan Amsbary EPRI [email protected]

Sarma (NDR)

Nuthalapati

PEAK [email protected]


Venkat Shastri NASPI Distribution Task Team/University of San

Diego

[email protected]

Olga Lavrova SNL [email protected]

Kofi Korsah ORNL [email protected]

Sydni Credle NETL [email protected]

Antonio Trujillo Eaton Corporation [email protected]

James Stoupis ABB [email protected]

Mirrasoul Mousavi ABB [email protected]

Initially, the working group was asked to review and comment on the relevant section of the Technology

Review and Assessment Document to become familiar with the current state of the art used in developing

the initially proposed research thrusts within the Asset Health Monitoring area. The team was then asked

to critically review and comment on the initially proposed set of research thrusts developed by the DOE

laboratory team and to provide insights into potential gaps that exist in terms of sensor device technology

within this area.

Based on team member input, a decision was made to refocus potential research thrusts around specific

parameters (e.g., temperature, dissolved gases in insulation oils, vibrations) to be measured rather than

application domains (e.g., large power transformer monitoring, conventional generator monitoring,

substation monitoring). The full list of the modified set of potential research thrusts considered appears in

Section D.3.3. The team then developed a set of quantitative metrics around the selected parameters

including (1) technical performance, (2) spatial characteristics, and (3) total cost of installation, among

others. Based upon the developed metrics, a survey of existing commercial sensors was performed to seek

what commercially available options could be identified, including the request of full quotations from
















D-12

vendors to gain access to estimated costs. These commercial sensors are not referenced explicitly in this

document because of sensitivities associated with proprietary information, but the information gathered

provided a basis for the key findings outlined, including the gap analysis summary table presented in

Section D.3.4.


The team identified many key findings during the working group process, which lead to the subsequent

proposed research thrusts. A summary of the most significant identified gaps, the proposed approaches to

address the gaps, and their linkages to the proposed research thrusts appear in the summary table in

Section D.3.4. Key findings include these:

1. There are many existing, commercial technologies for electrical grid asset health monitoring, but their

deployment is limited by the total cost of installation to assets for which the return on investment is

clear and obvious to the owner of the asset. Federal research efforts on asset monitoring of

electrical grid assets should specifically target (1) dramatic reductions in cost for comparable

performance to existing commercial technologies and (2) extremely low-cost sensing approaches

that can enable access to parameters of interest with adequate but reduced overall performance

levels.

2. Generation assets, such as fossil- and nuclear-based plants, impose extreme constraints on asset health

monitoring sensing technologies due to operational temperatures, pressures, erosive/corrosive

conditions, and the potential for radiation exposure. In contrast to electrical grid assets, only a very

limited number of commercial sensors exist that can satisfy these application requirements, yet their

increased requirement for flexible operation increases the need for real-time asset health monitoring.

Federal research efforts on asset health monitoring of conventional generation assets should

specifically target high-temperature and harsh environmental performance operational conditions

with cost as a secondary consideration.

3. Temperature is a key parameter in the early identification of faults and failures in assets across the

modern power system. Federal research efforts should target novel temperature sensing

approaches for internal asset monitoring through emerging technologies with unique

characteristics, such as compatibility with deployment internal to both electrical grid and

generation assets.


consequence events, such as manmade or natural events, to enable mitigation action that can prevent

large-scale failures and minimize impacts. Federal research efforts should target rapid, high-

bandwidth and low-latency electrical parameter measurements.

5. A unique value proposition exists for asset health monitoring sensors that (1) are capable of

monitoring multiple parameters of interest simultaneously (e.g., temperature, pressure, and gas phase

chemistry), (2) are compatible with internal electrical and generation asset deployment, and (3) enable

spatially distributed measurements. Federal research efforts should target sensor technology

platforms with these unique characteristics, such as optical and passive wireless sensor device

technologies as well as areal imaging–based techniques.

6. Indirect measurements of proxy parameters that are relatively easy and inexpensive to take are often

sufficient. Such instruments can take measurements external to an asset and provide insights about

asset health and faults/failures. Federal research efforts should encourage development of ultra-

low-cost proxy-based sensing platforms.

D-13

7. For well-established sensing technologies such as dynamic line rating systems, standards for data

management, data transfer, and communication can be a major barrier for widespread implementation

of new sensing and measurement technologies beyond the substation. Regulations that encourage

adoption of new, large capital grid assets may also inadvertently discourage the implementation and

adoption of sensing technologies that can be used to extract additional value from existing assets.

Federal regulations and standards should be critically reviewed to consider their potential impact

on new sensing and measurement technology deployment, including both intentional and

inadvertent impacts


Gaps identified by working

group

Relevant research

thrust or thrusts Approach to address gap

Key metrics to be

addressed

Grid asset monitoring

technologies exist, but

deployment is limited by cost

All Develop multi-tiered metrics to

balance performance/cost

tradeoffs

Dramatically reduce cost for

existing performance and enable

new lower-cost sensors with

reduced performance

Performance and

cost

Dissolved gas analysis (DGA)

plays a key role in asset health

monitoring of transformers but is

cost prohibitive

Research thrust 1: Real-

time DGA sensors

Develop DGA technologies of

varying performance for specific

application ranges but with

dramatically reduced costs

Performance and

cost

Nontraditional proxies can be

deployed for early detection of

fault conditions

Research thrust 6:

Vibration event detection

Research thrust 7:

Acoustic event detection

Develop low-cost proxies that

can be ubiquitously applied to

grid assets

Efficacy as a proxy,

cost

Local monitoring of utility pole

and line orientation can enable

prevention of failures and more

rapid recovery and restoration

times

Research thrust 10: Pole

tilt and line sag

monitoring

Develop low-cost tilt sensors for

poles and lines that can be

ubiquitously applied to grid

assets

Performance and

cost.

Non-localized signatures of

failures or faults are difficult to

detect with individual sensors

Research thrust 8: Areal

temperature monitoring

through imaging

Research thrust 9: Areal

gas insulation leak

monitoring through

imaging

Research thrust 11: Line

temperature profile

Research thrust 12: Line

acoustic monitoring

Develop techniques that enable

areal imaging or linear mapping

of parameters of interest with

optimal trade-offs in spatial

resolution, cost, and performance

Areal or linear

spatial resolution,

performance, and

cost

Thermal signatures are a primary

indicator of grid asset health

faults/failures, but internal

temperatures exhibit

characteristic hot spots that can

be difficult to detect

Research thrust 2: Grid

asset internal temperature

Develop multipoint temperature

sensor technologies and

extremely low-cost single-point

sensor technologies for improved

monitoring

Number of sensor

nodes and cost

D-14


group

Relevant research


Key metrics to be

addressed

Energy storage will play an

increasingly key role in grid

resiliency and stability moving

forward

Research thrust 13:

Internal chemistry

(energy storage)

Research thrust 14: State

of charge (energy

storage)

Develop new sensor

technologies capable of real-time

monitoring of energy storage

performance/ degradation

Performance, cost,

and compatibility

with internal energy

storage deployment

Existing generation plant

monitoring will be increasingly

important in the future because

of needs for more flexible

operation


asset internal temperature


asset internal strain

Research thrust 7:

Acoustic event detection

Research thrust 15: Boiler

water chemistry

monitoring

Several specific metrics were

developed around the needs of

internal monitoring of

centralized generators. A

specific research thrust was also

developed for boiler water

chemistry monitoring

High-temperature

compatibility,

performance

Electrical parameters can

provide the most rapid signatures

of low-probability, high-

consequence events, such as

human or natural threats (e.g.,

geomagnetic disturbance,

electromagnetic pulse)

Research thrust 4: Fault-

current detection

Research thrust 5:

Under/overvoltage

transient monitoring

Development of rapid, high-

bandwidth, and low-latency

electrical parameter

measurements with sufficiently

low cost for ubiquitous

deployment

Performance,

latency, bandwidth,

and cost

Regulations that promote

deployment of new sensing

technologies rather than

replacement of large existing

capital assets

Recommendation made to

the crosscutting working

group

Provide a forum for discussing

business model challenges for

new sensor deployment by

industry

Regulation efficacy

and awareness by

regulation bodies

Standardized data and

communication protocols for

new sensors not integrated

within components or

substations

Recommendation made to

the crosscutting, data

management, and data

analytics working groups

More clearly communicate the

challenges associated with using

new sensor data as a barrier to

deployment and implementation

Standard efficacy

and awareness by

standards

organizations


Based upon the results of the working group efforts and the associated gap analysis, a total of 15 different

research thrusts were initially developed for consideration and discussion, as listed below. To minimize

overlap with other focus areas, this number was condensed and reduced to ten different research thrusts.

Of these research thrusts, seven are being recommended for prioritization, as indicated below.

Research thrusts developed (initial):

1. Real-time dissolved gas analysis (DGA) sensors

2. Grid asset internal temperature

3. Grid asset internal strain

4. Fault-current detection

5. Under/overvoltage transient monitoring

6. Vibration event detection

7. Acoustic event detection

8. Areal temperature monitoring through imaging

D-15

9. Areal gas insulation leak monitoring through imaging

10. Pole tilt and line sag monitoring

11. Line temperature profile

12. Line acoustic monitoring

13. Internal chemistry (energy storage)

14. State of charge (energy storage)

15. Boiler water chemistry monitoring

Research thrusts developed (after combining with other working groups):

1. Real-time dissolved gas analysis sensors (recommended)

2. Grid asset internal temperature (recommended)

3. Grid asset internal strain

4. Acoustic and ultrasonic vibration event detection (recommended)

5. Areal temperature and gas insulation leak monitoring through imaging

6. Pole tilt and line sag monitoring (recommended)

7. Line temperature profile (recommended)

8. Line acoustic monitoring

9. Internal chemistry (energy storage) (recommended)

10. Boiler water chemistry monitoring (recommended)

D.3.6 Relation with Existing GMLC/GMI Efforts

The proposed research thrusts for prioritization are being addressed to some extent through existing

efforts supported under the GMLC and the GMI more broadly. Research focused on real-time DGA

sensors (research thrust 1), as well as the grid asset internal temperature (research thrust 2) sensors is

being pursued under the GMLC Advanced Sensor Development Project by leveraging the microwave

surface acoustic wave sensor-based platforms (ORNL) and the optical fiber-based platforms (NETL). An

existing effort under the GMLC Advanced Sensor Development project also targets passive microwave

sensor technology, referred to as “MagSense,” for ubiquitous grid asset fault current monitoring. An

existing program is also being carried out under a recent solicitation by DOE’s Office of Electricity: it

targets advanced distribution sensors, on the topic of optical fiber–based sensors, in a program being led

by PARC in collaboration with General Electric. Despite the ongoing efforts in this area within the

GMLC and GMI, clear opportunity exists to expand upon the area of asset health monitoring to address

the targeted research thrusts recommended by the working group.

D.4 PHASOR MEASUREMENT UNITS FOR GRID STATE AND POWER FLOW

(LABORATORY LEAD: YAROM POLSKY, ORNL)


Phasor measurement units (PMUs) are a critical enabling technology for providing system visibility and

control capability. They have become more widely used to measure and time-stamp basic electrical

parameters in modern systems since 2009. But significant improvements in both performance and cost are

still required to achieve grid modernization goals related to situational awareness and dynamic, real-time

control. Historical use of PMUs has primarily focused on post-mortem diagnosis of grid events. The cost-

reduction and performance improvement goals described in the subtopics of this focus area are intended

to catalyze wider and more rapid adoption of PMUs across the grid and to enable novel dynamic control

implementations that significantly enhance observability, control, and reliability. This working group

reviewed the current proposed PMU research thrusts areas with respect to the current industrial state of

the art and quantitative metrics for new PMU technology development.

D-16


The PMU working group established a core group of stakeholders spanning the DOE national laboratory

system, academia, utilities, and vendors. The following is a full list of participants.


Alireza Shahsavari University of California–Riverside [email protected]

David Schoenwald SNL [email protected]

Emma Stewart LLNL [email protected]


Evangelos Farantatos Electrical Power Research Institute [email protected]

Felipe Wilches-Bernal SNL [email protected]

Gordon Mathews Bonneville Power Administration. [email protected]

Jaya Yellajosula Michigan Tech [email protected]

Jerry Fitzpatrick NIST [email protected]

Junbo Zhao NASPI Distribution Task

Team/PNNL

[email protected]

Linwei Zhan ORNL [email protected]

Reza Arghandeh Florida State University [email protected]

Sarma Nuthalapati Peak [email protected]

Sascha von Meier University of California–Berkeley [email protected]

Shaun Murphy PJM [email protected]

Sumit Paduyal Michigan Tech [email protected]

Tom Rizy ORNL [email protected]

Venkat Krishnan NREL [email protected]

Yarom Polsky ORNL [email protected]

Yilu Liu University of Tennessee [email protected]


Review and Assessment Document to familiarize members with the current state of the art used in

developing the initially proposed research thrusts within the PMU area. The team was then asked to

critically review and comment on the initially proposed set of research thrusts developed by the DOE

national laboratory team and to provide insights into potential gaps that exist in terms of sensor device

technology within this area.

Based on team member input, a decision was made to consolidate the initial five thrust areas into three

based on primary PMU characteristics of interest: performance, cost, and reliability. In some respects, the

PMU topic is relatively narrow and the technology is relatively mature. On the other hand, the

information provided by PMUs is critical to grid monitoring and control, and the technology requires

significant improvements to increase both its market penetration and performance that enables improved

situational awareness and power flow control of the grid. For example, while PMU coverage of the

transmission system is reasonably complete, coverage in the distribution system is sparse to nonexistent.

Realizing this more granular observability of grid power state is primarily hampered by challenges

associated with PMU costs, including installation and operation and maintenance (O&M). Additionally,

high-speed, real-time control applications necessitate an estimated 1 to 2 order of magnitude improvement

in PMU dynamic performance and reliability. The proposed improvements should be evaluated both





















D-17

individually and collectively since, in some instances, they may be at odds with each other (e.g.,

performance vs. cost). The interrelationships between thrusts and gaps should similarly be considered in

the following gap analysis and recommendations.


The team identified a number of key findings during the working group process, which led to the

proposed research thrusts. A summary of the most significant identified gaps, the proposed approaches to

address the gaps, and their linkages to proposed research thrusts can be found in the summary table in

Section D.4.4. Key findings include these:

1. The proposed dynamic performance requirements for PMUs are currently based on academic studies,

since actual controls demonstrations using PMU data are limited. Future dynamic and distributed

controls demonstrations will permit refinement of PMU performance parameters against real-world

data sets. Federal research efforts should periodically reevaluate the dynamic performance

requirements of PMUs based on advanced controls implementations and demonstrations.

2. While the unit cost ranges of PMUs are known and easily updated, life cycle costs of PMUs—

including installation, operation, and maintenance—are less certain. Federal research efforts focused

on lowering the costs of PMUs should develop more comprehensive, data-based life cycle cost

models of PMUs to formulate more accurate and relevant cost targets.

3. There are a significant number of PMUs that have already been installed and are in commercial use.

Most of these systems do not meet the target performance and reliability metrics proposed in the

research thrust areas. Federal research efforts focused on lowering the costs of PMUs should also

consider both the costs and benefits associated with retrofitting existing PMU installations.

4. The proposed reliability metrics should be more formally evaluated and refined. In particular, the

proposed reliability metrics should be considered in the context of both existing IEEE timing

standards and future control and situational awareness goals. Federal research efforts should develop

a justification for proposed reliability metrics.



group

Relevant research


Key metrics to be

addressed

Performance metrics are

aspirational and need to be

updated as controls applications

evolve

Improve the dynamic

response and accuracy

of PMUs

Reevaluate metrics against

findings of pilot controls

projects

All

Cost data are incomplete or need

to be updated—particularly

O&M costs

Lower the cost of

PMUs

Industry survey Cost

There may be a need to

differentiate and consider cost

metrics with regard to both new

PMU installations and retrofits

Lower the cost of

PMUs

Market evaluation and

preliminary scoping study

Retrofit cost vs. new

system cost

Reliability metrics need to be Improve PMU timing Need to develop case and Timing service reliability

D-18


group

Relevant research


Key metrics to be

addressed

justified reliability justification for specified

metrics. These must be

considered with respect to

current IEEE timing

standard


Based upon the results of the working group efforts and the associated gap analysis, a total of three

different research thrusts were developed for consideration and discussion, as follows.

Research thrusts developed:

1. Improve the dynamic response and accuracy of PMUs (recommended)

2. Lower the cost of PMUs (recommended)

3. Improve PMU timing reliability (recommended)


One currently funded project, Advanced Sensor Development, in the Sensing and Measurement GMLC

technical area, has a subset of tasks related to PMU algorithm development. Specifically, it aims to

“develop advanced PMU algorithms for ultra-fast transient measurements during disturbances and to

integrate PMU algorithms into optical transducers for high-accuracy steady state monitoring.” This

project does not directly address the goals of the proposed thrust areas but is considered to be

complementary with respect to its performance goals. There are also three syncrophasor data end-use

projects currently funded through GMLC. One is focused on developing tools for more efficiently using

syncrophasor data and the other two projects are focused on applications of syncrophasor data. One

investigates high-voltage direct current load modulation using syncrophasor data, and the other seeks to

improve situational awareness of grid state by applying machine learning to syncrophasor data sets. There

are no active projects focused specifically on PMU improvement or cost reduction, to the knowledge of

the working group.

D.5 NOVEL TRANSDUCERS—ENCOMPASSES SENSORS FOR DYNAMIC SYSTEM

PROTECTION, GRID ASSET FUNCTIONAL PERFORMANCE MONITORING,

SENSORS TO ENABLE ADVANCED GENERATION CONTROLS, NOVEL VOLTAGE

AND CURRENT TRANSDUCERS (LABORATORY LEAD: OLGA LAVROVA, SNL)


Novel electrical transducers can have an impact across a broad range of applications and use cases in the

transmission and distribution system. To explore synergies and crosscutting opportunities, this working

group focused on the development and application of novel voltage and current transducers across

proposed focus areas, including (1) dynamic system protection, (2) grid asset functional performance

monitoring, and (3) enabling of advanced controls and functionality multiple assets and coordination

between them. This working group reviewed the current proposed research thrusts within these focus

areas of the Roadmap and developed a clear understanding of the current industrial state of the art and

quantitative metrics for new sensing and measurement technology development. The group made

D-19

recommendations regarding opportunities for leveraging synergies across the originally identified focus

areas encompassed by this broader topical area.


The Novel Transducers working group established a core group of stakeholders spanning the DOE

national laboratory system, academia, utilities, and vendors. The following is a full list of participants.

Name Organization Email


Farnoosh Rahmatian NuGridPower/NASPI PRSVTT [email protected]


Alireza Shahsavari NASPI Distribution Task Team/

University of CA–Riverside

[email protected]

Tim McIntyre ORNL [email protected]

Peter Fuhr ORNL [email protected]

Marissa Morales-Rodriguez ORNL [email protected]

Venkat Shastri NASPI Distribution Task Team/

University of San Diego

[email protected]

Jack Flicker SNL [email protected]


Junbo Zhao NASPI Distribution Task Team/PNNL [email protected]



Harold Kirkham PNNL [email protected]



Initially, the working group was asked to compile a comprehensive list of distribution grid sensors that

are currently commercially available, both commercial-off-the-shelf and state-of-the-art. The team was

then asked to critically review and comment on functional gaps present in the list and then provide

insights into potential gaps that exist in terms of sensor device technology within this area.


During the working group process, the team identified key finding , which led to proposed research

thrusts. The summary table in Section D.5.4 summarizes the most significant identified gaps, proposed

approaches to address the gaps, and their linkages to proposed research thrusts. Key findings are these:

1. At the core of smart power distribution systems are smart devices that enable facility managers to

take preventive measures to mitigate potential risks. These devices have become more than just

responsible for controlling a single mechanism. They now measure and collect data and provide

control functions. Furthermore, they enable facility and maintenance personnel to access the power

distribution network.

2. Many existing commercial technologies (transducers and sensors) exist for electrical grid monitoring,

including monitoring at the grid-edge. However, the usability of information produced and reported

















D-20

by these transducers and sensors is limited because of the lack of a framework for information

reporting. Translating information into actionable information also is constrained by the lack of a

framework. Federal research efforts on novel transducers and electrical parameter sensors for

electrical grid assets should specifically target (1) transducers and sensors providing actionable

information and 2b) a unified framework of parameter reporting and information processing.


consequence events, man-made or natural. These measurements can enable preventative action to

prevent large-scale failures and minimize impacts, resulting in increasing grid resiliency. Federal

research efforts should target rapid, high-bandwidth and low-latency electrical parameter

measurements.

4. In most cases, abnormal behavior (e.g., failures, faults, or severe degradation of performance of an

asset) manifests itself in a deviation from nominal operating frequency or the presence of abnormal

frequencies (such as new harmonics or completely new frequency characteristics). Detecting such a

frequency is a key parameter in the early identification of faults and failures in assets across the

modern power system. Federal research efforts should target novel frequency-selective sensors that

can provide fundamentally new information (relative to sensing at 60 Hz).

5. On the opposite side of the spectrum, extremely low-cost but ubiquitous transducers could provide

single-parameter reporting at a significantly low cost. Big data processing methods can be extremely

useful for processing substantial amounts of single-parameter data over large geographical scales and

translating these data into actionable information across balancing authority or regional control area

scales. Federal research efforts should specifically target extremely low-cost sensing approaches

that can enable access to parameters of interest with adequate but reduced overall performance

levels.

6. A unique value proposition exists for sensors that (1) are capable of monitoring multiple parameters

of interest simultaneously, (2) are compatible with internal electrical and generation asset

deployment, and (3) enable spatially distributed measurements. Federal research efforts should

target sensor technology platforms with these unique characteristics, such as optical and passive

wireless sensor device technologies and areal imaging based techniques.


Gaps identified by

working group

Relevant research


Key metrics to be

addressed

Grid asset

monitoring

technologies exist,

but deployment is

limited by cost

All Develop multi-tiered metrics to

balance performance/cost trade-offs

Dramatically reduce cost for existing

performance and enable new lower-

cost sensors with reduced

performance

Performance and cost

Fast-acting

broadband sensors

for dynamic

system protection

These sensors must

quickly sense and

transmit their data,

so that relays and

switches can be

Research thrust 1:

Frequency-selective

current sensing

Research thrust 2:

Fault-current detection

Research thrust 3:

Location of the fault

detection

Research thrust 4:

Development of frequency-selective

high-bandwidth, and low latency

electrical current measurements with

sufficiently low cost for ubiquitous

deployment

Frequency range,

dynamic range for

voltage and current,

latency, cost

D-21

Gaps identified by

working group

Relevant research


Key metrics to be

addressed

activated to protect

grid equipment

from damage.

Sensors must be

capable of

detection with

performance (e.g.,

response time,

accuracy,

precision) to meet

the requirements of

adaptive protection

schemes

Optical CT/PTs

Performance

sensors for next-

generation (solid-

state) transformers

Research thrust 5:

Accurate harmonics

and total harmonic

distortion (THD)

measurement

Research thrust 6:

Accurate pulse width

modulation (PWM)

diagnostics

Development of a new set of

transducers capable of providing

accurate information about

frequency content and THD

Frequency range,

dynamic range, latency,

cost

Derivative sensors

Like ROCOF (rate

of change of

frequency),

derivative sensors

for voltage and

current may be

very useful for

utilities for

monitoring of

dynamic operating

states

Research thrust 7:

Voltage derivative

sensors

Research thrust 8:

Current derivative

sensors

Research thrust 9:

Frequency derivative

sensors (ROCOF)

Develop a new set of transducers

capable of providing information

about rates of changes (dynamic) of

voltage, current, and frequency.

Frequency range,

dynamic range, latency,

cost

Electrical

parameter

measurements for

energy storage

Energy storage will

play an

increasingly key

role in grid

resiliency and

stability moving

forward

Research thrust 10:

State of

charge/discharge

(energy storage)

Research thrust 11:

Rate of

charge/discharge

(energy storage)

Research thrust 12:

Depth of discharge

(energy storage)

Research thrust 13:

Cumulative (lifetime)

number of

charge/discharge

cycles (energy storage)

Research thrust 14:

Cumulative (lifetime)

Develop new sensor technologies

capable of real-time monitoring of

energy storage performance/

degradation

Performance, cost,

compatibility with

internal energy storage

deployment

D-22

Gaps identified by

working group

Relevant research


Key metrics to be

addressed

kWh (energy storage)

Behind-the-meter

(customer) sensing.

Transducers

creating actionable

information from

all the new smart

devices, which

may be installed

behind the meter

(customer) location

Research thrust 15:

Novel behind-the-

meter transducers

Solutions that monitor performance

of several devices and broadcast this

information to the utility. A possible

smart outlet, which can collect

power and power quality

information, is another example. A

complete solution would be a smart

meter, which provides not only

revenue information but also power

and power quality information for all

devices at the customer’s

interconnection location

System integration of

the sensors and cost

Standardized data

and

communication

protocols for novel

transducers not

integrated within

components or

substations

Recommendation

made to the

crosscutting, data

management, and data

analytics working

groups

More clearly communicate the

challenges associated with using

new transducer data as barriers to

deployment and implementation

Standard efficacy,

awareness by standards

organizations, “death by

big data”


Based upon the results of the working group efforts and the associated gap analysis, a total of 15 different

research thrusts (listed below) were developed for consideration and discussion. Based on further

discussions within this workgroup and in discussion with other workgroups, ten of the initially proposed

workgroups (listed below) were merged, renamed, and recommended to proceed.

Research thrusts developed initially:

1. Frequency-selective current sensing

2. Fault-current detection

3. Location of the fault detection

4. Optical current transformers/potential transformers

5. Accurate harmonics and THD measurement

6. Accurate PWM diagnostics

7. Voltage derivative sensors

8. Current derivative sensors

9. Frequency derivative sensors (ROCOF)

10. States of charge/discharge (energy storage)

11. Rate of charge/discharge (energy storage)

12. Depth of discharge (energy storage)

13. Cumulative (lifetime) number of cycles (energy storage)

14. Cumulative (lifetime) kWh (energy storage)

15. Novel behind-the meter transducers

Research thrusts developed after consolidation, all of which are recommended to proceed:

D-23

1. Fast-acting current sensors for fault detection and dynamic system protection

2. Fast-acting voltage sensors for fault detection and dynamic system protection

3. Grid asset health and performance monitoring (traditional transformers)

4. Performance sensors for next-generation (solid state) transformers

5. Electrical parameter measurements for energy storage

6. Fast-acting sensors (other than voltage and current) for dynamic system protection

7. Derivative sensors

8. Broadband frequency-selective current sensor

9. Behind-the-meter (customer) sensing

10. Maturation of all-optical transducer technologies



efforts supported under the GMLC and, more broadly, the GMI. In particular, an existing effort under the

GMLC Advanced Sensor Development project also targets passive microwave sensor technology, or

MagSense, for ubiquitous grid asset fault current monitoring (research thrust 6).

Despite the ongoing efforts in this area within the GMLC and GMI, a clear opportunity exists to expand

upon the area of development of novel transducers that can sense and communicate new actionable

information, which will lead to more informed and robust electric grid and asset controls.

D.6 WEATHER MONITORING AND FORECASTING (LABORATORY LEAD: VENKAT

KRISHNAN, NREL)


Increasing penetrations of weather-dependent renewable energy sources are making weather sensors even

more important for monitoring and predicting generation. Installed capacities of solar photovoltaic (PV),

concentrating solar power (CSP), and wind energy have grown significantly in recent years, to the point

that they have a significant impact on generation profiles. Grid integration of these renewable energy

systems benefits from the operational awareness provided by real-time sensing of both wind and solar

resources and energy production, as well as forecasting from weather prediction over time scales from 0–

5 minutes to 24–48 hours ahead. Additionally, weather forecasts provide valuable information for

forecasting electricity consumption. This working group will review the current proposed research thrusts

within this focus area of the Sensing and Measurement Technology Roadmap and develop a clear

understanding of the current industrial state of the art and quantitative metrics for new sensing and

measurement technology development.


The Weather Monitoring and Forecasting working group established a core group of stakeholders

spanning the DOE national laboratory system, academia, industry, and sensing instrumentation vendors.

The following is a full list of participants.

Person Organization Expertise

Manajit Sengupta NREL Sensors for wind and solar forecasting

Dan Riley and Matt Lave SNL PV sensing and measurements

Tim McIntryre ORNL Sensors for harsh environments


D-24

Person Organization Expertise

Jan Kleissl University of CA–San Diego

(UCSD)

Weather station network at the UCSD campus

Steve Miller Colorado State University Satellite remote sensing of solar radiation

Qilong Min University at Albany–Sunny Weather sensing and NY mesonets for resilience

Melinda Marquis National Oceanic and

Atmospheric Administration

Weather forecasts for wind and solar power

applications, at Earth System Research

Laboratory

Sue Haupt National Center for

Atmospheric Research

Wind and solar forecasting from Weather

Research and Forecasting model

Aidan Tuohy EPRI Forecast integration into independent system

operator and utility operations

Gail Vaucher Army Research Laboratory Sensors in harsh environments

Justin Robinson GroundWork Renewables

Meeting during NREL’s annual Pyrheliometer

Comparisons event: Expertise in solar monitoring,

measurement, instrumentation, data logging and

data processing–utility-scale PV

Erik Naranen ISO-CAL North America (lab

quality manager)

Tom Kirk Eppley Lab, President

Wim Zaaiman Joint Research Centre, Italy

Victor Cassella Kipp & Zonen USA Inc.

Chris Kern Irradiance

Josh Peterson University of Oregon

Aron Habte, Mike Dooraghi,

Mark Kutchenreiter

NREL

The working lead had several meetings with each of the experts, either in person or electronically, to

review the roadmap content and research thrusts and solicit input on the following questions:

• Are there any important measurement parameters and sensing technologies that have been left out of

the technology review document and the roadmap document, specifically related to harsh

environments such as mountains, arctic conditions, and offshore wind systems?

• Are the research thrusts identified the most important ones? If yes, why? If not, why? What other

research thrusts can be included?

• Are the quantitative metrics identified in the research thrusts valid and viable to achieve?

• Can you point to some past and current studies/literature related to weather monitoring and

forecasting that could be used to update the technology review document?

Each of these conversations, including direct edits to the review document provided by some of the

experts, laid the foundation to revise the technology review document, perform gap analysis between

current state of the art and future needs, develop high-priority research thrusts in this focus area, and

update the roadmap document.


Based on team member input, the major comments or recommendations included the following three

research thrusts:

D-25

1. Optimal allocation of sensors considering cost and reliability: It is vital to consider the vast

amount of existing weather-monitoring sensor and measurement infrastructure and find ways to

harness it for various grid modeling and operational purposes, before we seek to deploy newer

weather-monitoring infrastructures.

2. Developing reliable sensors for harsh environments: Highly reliable weather-monitoring

instrumentation already exists. In considering the cost and reliability of a weather sensing device, it

will be important to look at the entire system (including communication and data processing) rather

than just particular devices. The reliability of such sensor systems, especially in remote harsh

environments, depends largely on resources being allocated, such as maintenance budget, personnel,

robust communication channels, error detection in data assimilation process, and quality checks. The

reliability needs and associated maintenance budgets may be dictated by the end-use applications.

3. Distributed smart sensors (with onboard analytics): While this concept may seem interesting,

given that many sensing technologies with onboard analytics already in use, the use case and benefits

are hard to see. There should be a thrust to understand the requirements of weather sensors in terms of

reliability, accuracy, communication latency, local vs. central data analytics ability, and so on, for

various grid applications.

Specific suggestions were made to include research thrusts for advancing variable renewable forecasts,

and uncertainty quantification and grid-edge resource observability. Additionally, concerns were raised

regarding integrating the available weather monitoring data into grid operation and decision-making

platforms in the form of advanced forecasts and visualizations for situational awareness and grid

resilience. Specifically, suggestions were provided to better represent the integration of severe weather

event data such as floods, lightning, fire, and storms. Such integration will serve as a driver for further

innovations in the weather-monitoring and forecasting area by pushing the boundary on current sensing

system performances.

The table in Section D.6.4 presents detailed descriptions of the gap analysis.



group

Relevant research


Key metrics to be

addressed

Many weather-monitoring

and measurement resources

exist in the nation, but

awareness and collaboration

are needed to use them for

various grid modernization

applications

Technologies for scalable

deployment and grid-edge

observability need to be

researched

Research thrust 1:

Harnessing existing

weather monitoring

resources

Harness existing weather

monitoring resources (e.g.,

satellite data, mesonets, weather

stations) by creation of a

consortium made up of key

personnel responsible for data

generation, communication,

assimilation and end use

Research low-cost technology

options and scalable deployment

Research innovative technology

integration and portable high-

quality calibration techniques

for various applications

Facilitate public and private data

partnership

Data availability at

various spatial and

temporal resolutions

Cost of data

acquisition

D-26


group

Relevant research


Key metrics to be

addressed

Comprehensively document

disparate data resources by key

measurement parameters

Standardization of weather

monitoring and forecasting

data needs attention, because

this enables efficiency and

innovation. Standardization

must be extended from

meteorological to forecast

data reporting

Research thrust 2:

Framework for

disparate data

processing and

standardization for

utility integration

Work with utilities and

independent system operators

(ISOs) to understand format

variations and rationales

Develop a framework for

distributed data assimilation of

weather data, quality assurance,

data analytics for applications,

and derived data reporting

format standardization

Cost of data storage

and maintenance

Data curating rates

(processing and

quality assurance)

Better integration of weather-

monitoring data is needed for

various grid modernization

applications at both

transmission and distribution

systems

All research thrusts 1–5 Understand the weather

monitoring resources

Develop a framework to ingest a

wide variety of data

Improve forecast models and

visualizations and integrate

them into various ISO and

utility operations and planning

applications

Forecast accuracy

Cost of data

acquisition

Data availability

Power system

operational economics

and reliability

Innovative forecasting

models that not only forecast

the mean power but also its

ramps and associated

uncertainties are needed,

along with their integration

into grid decision support

processes

Research thrust 3:

Advanced forecasting

models and their

integration

Develop advanced forecasting

models for probabilistic

forecasts of load, variable

renewables and net-load power

and ramps

Use big data analytics in

conjunction with numerical

weather prediction for

developing probabilistic forecast

models

Work with industry to evaluate

the value proposition and

recommend best practices of

forecast integration

Validate satellite data–based on-

ground mounted sensors and

improve the spatial and

temporal resolutions of

forecasting models

Forecast accuracy

Uncertainty

quantification of mean

power and ramp

forecasts

Lean reserve

procurements for grid

operation

Observability of grid

topology and states for

various utility

applications

Improved system

reliability

A decision support tool is

needed to enable situational

awareness and timely

decision making by system

operators to ensure reliability

Research thrust 4: Real-

time visualization and

situational awareness

(Overlaps with research

thrust 3: Advanced

Work with ISO and utility to

develop visualization software

that can integrate live forecast

feeds into energy management

system and distribution

Short-term forecast

accuracy

Rate of forecast

updates

D-27


group

Relevant research


Key metrics to be

addressed

now and in the future,

enabling transactive markets

at the customer level

forecasting models and

their integration, and

with

thrust 5: Establishing

requirements and

optimizing weather

sensing infrastructure

for different smart grid

applications)

management system platforms

Evaluate value proposition and

recommend best practices for

operational decision making

with visualization tools

Train operators

Develop onboard data analytics

for sensing systems, for faster

communication and local

decision making

Accuracy of ramp

alerts

Better visibility of

behind-the-meter

generation and

demand resources

Grid resilience (local

decision making)

There are several grid

modernization applications

that can benefit from timely,

reliable, and accurate

weather-monitoring and

forecast data, but the

challenge is to understand the

requirements of weather data

accuracy, quality, and

reliability for these

applications

Research thrust 5:

Establishing

requirements and

optimizing weather

sensing infrastructure

for different smart grid

applications

Develop hybrid (physics-based

and data-driven) models that

relate grid applications and

weather-dependent parameter

forecasts or state estimates

Understand the impacts of

different resources, varying

reliability, data coverage, and

sensing infrastructure cost on

application performance

Investigate the weather-

monitoring data requirements

for interdependent (or

convergent) infrastructure

systems (energy, fuel, gas, water

and transportation)

For each application:

Data reliability

Maintenance budget

Coverage (spatial and

temporal resolutions)

Forecast accuracy

Total cost of sensing

infrastructure

ownership

Data quality and

retrieval

Data error detection

and recovery


The working group recommended considering the sensing devices/instrumentation, data processing,

communication, and system integration of direct or derived measurements as a single system to assess the

gaps in the system and recommend the future research thrusts required. Because it is not enough to make

just the sensing instrumentation cost-effective or reliable, the entire system should be made reliable and

cost-effective for grid modernization use cases. From this perspective, the working group recommended

the following structure for the Roadmap: Begin with the use cases that drive the need for sensing system

innovations, and then go down to each of the unique sensing systems that enable the use cases. Under

each sensing area, mention the associated high-priority research thrusts. Under such a structure, the

thrusts for the weather sensing area that span device needs, as well as advanced data driven modeling and

integration, would remain together.

However, given that the roadmap structure currently articulates the thrusts under devices, communication,

and data-driven models as separate sections, the research thrusts identified in the weather-monitoring

focus area are divided into Devices and Data-Driven Modeling or Analytics sections. Thrusts relevant to

developing sensing devices for additional parameters, or integrating innovative, low-cost, or highly

reliable sensors will go under the Devices section. Thrusts relevant to using sensor data for advancing

physics-based weather phenomena models—including advanced forecast models and their uncertainty

D-28

characterization, harnessing disparate data, severe events data, and standardization for utility

integration—will all be under the Data-Driven Modeling section. In addition to the uniqueness of the

research needs for weather data integration mentioned in these sections, any apparent overall theme that

may be common to other sensing areas (e.g., standardization and value proposition), is mentioned under

the crosscutting initiatives.

The following seven research thrusts were identified as high in priority. They are divided into three

sections.

Devices

1. Integration and testing of innovative low-cost weather sensing technologies

2. Development of devices for enhanced weather observability

Data-driven modeling and integration

1. Harnessing existing disparate weather- monitoring resources and enabling their optimal use

2. Advanced modeling of resource observability and forecasting

3. Integrating high-impact weather situations for grid resilience

Crosscutting

1. Weather measurement standardization and quality control

2. Establishing requirements for different grid applications

Full descriptions of these research thrusts and the associated activities are given in the Roadmap. Thrusts

6 and 7 are not explicitly mentioned in the document, but they are emphasized in crosscutting initiatives

in a broader generic context.

Additionally, a use case for better integration of weather data for power grid modernization is

recommended for applications relevant to grid dispatch, flexibility, situational awareness, and resilience.

This use case emphasizes the need and importance of efficiently integrating weather data for economic

and flexible operation—on a minute-by-minute, hour-by-hour, and day-to-day basis—of future power

grids with highly variable renewable penetration.

D.6.6 Relationship with Existing GMLC/GMI Efforts

The proposed research thrusts are being addressed to some extent through existing efforts supported under

the GMLC and the GMI, especially the thrusts related to the development of advanced forecasting models

and situational awareness. Research focused on forecasting and visualization is being pursued under

GMLC category 2 projects funded by the wind (Wind Technologies Office) and solar (Solar Energy

Technologies Office) programs. Additionally, there was a 2017 award announcement from SETO on

Solar Forecasting II30 that focused on development of probabilistic solar irradiance and power forecasting,

grid integration, and validation methods. A 2018 funding opportunity announcement from SETO (FOA

1840)31 asked for advanced methods and validations for improving grid-edge solar observability.

However, according to the gap analysis mentioned earlier and the recommended research thrusts, lower-

technology readiness level R&D is needed to develop low-cost weather sensors for scalable deployment

and to enhance observability, develop high-quality calibration and sensing for critical applications,

30 https://www.energy.gov/eere/solar/funding-opportunity-announcement-solar-forecasting-2 31 https://www.energy.gov/eere/solar/funding-opportunity-announcement-fy-2018-solar-energy-technologies-office

D-29

harness disparate sensing resources for optimal integration of forecasts, integrate severe weather data,

improve situational awareness in energy management system and distribution management system

environments, and promote standardization.

D.7 END-USE/BUILDINGS MONITORING (LABORATORY LEAD: GUODONG LIU, ORNL)


Smart meters provide utilities with the ability to monitor the operating status of distribution systems as

well as end users’ energy consumption for steady-state operation. However, distributed generation and

energy storage control, system dynamics, islanding, and resynchronization of microgrids/nanogrids

require the deployment of much faster and higher-resolution (e.g., millisecond) sensors. These sensors

should be able to provide the data needed for advanced applications, such as seamless islanding and

resynchronization of microgrids. To enable optimal end-use building electric load operation and

coordination with the utility distribution system, multi-component sensors that are integrated, interactive

and intelligent need to be developed for comprehensive self-learning/adaptive controls, transactive

energies, and so on. This working group will review currently proposed research thrusts within this focus

area of the Sensing and Measurement Technology Roadmap and develop a clear understanding of the

current industrial state of the art and quantitative metrics for new sensing and measurement technology

development.


The End-Use/Buildings Monitoring Working Group established a core group of stakeholders spanning the

DOE national laboratory system, academia, utilities, and vendors. The following is a full list of

participants.



Lilian Bruce Electric Power Board of

Chattanooga

[email protected]


Sumit Paudyal Michigan Tech [email protected]

Tim McIntryre ORNL [email protected]

Teja Kuruganti ORNL [email protected]

Guodong Liu ORNL [email protected]



the initially proposed research thrusts within the End-Use/Buildings Monitoring area. The team was then

asked to critically review and comment on the initially proposed set of research thrusts developed by the

DOE national laboratory team and provide insights into potential gaps in sensor device technology. A

previous roadmap of DOE’s Building Technologies Office served as a reference.

Based on team member input, three key research thrusts were proposed. For each research thrust, the

working group focused on the measured parameters and key metrics, including measured data resolution,

measurement accuracy, and fully installed cost. The information gathered provided a basis for the key

findings outlined below, including the gap analysis summary table presented in Section D.7.4.



D-30


The team identified many key findings during the working group process, which led to the proposed

research thrusts. A summary of the most significant gaps, the proposed approaches to address these gaps,

and the linkages to proposed research thrusts appear in the summary table in Section D.7.4. Key findings

include the following.

1. Many commercial technologies exist for end-use/building monitoring. Their deployment is limited by

the total cost of installation. Federal research efforts on end-use/building monitoring should

specifically target (1) dramatic reductions in cost for comparable performance to existing

commercial technologies and (2) extremely low-cost sensing approaches that can enable access to

parameters of interest with adequate but reduced overall performance levels.

2. Microgrids, building microgrids, and nano-microgrids will need high-resolution current and voltage

sensors for advanced control, such as islanding and resynchronization. Federal research efforts

should target high-resolution and high- accuracy current/voltage sensors with modest cost.

3. Wireless, self-powered, self-configuring, self- commissioning, and self-calibrating sensors for

building efficiency will be necessary for future transactive controls. Federal research efforts should

target development of low-cost, wireless, self-powered, self-calibrating sensors for large-scale

deployment.

4. Multiple building sensors (e.g., temperature, humidity, air quality) could be integrated on the same

chip to lower cost and supplement intelligent building control functions. Federal research efforts

should target multi-component, integrated, low-cost sensors for building efficiency.

5. Electricity, temperature, luminance, air quality, building occupancy, and other values are measured by

different types of equipment and typically are not correlated to perform advanced functions like fault

detection and diagnosis (FDD) of building equipment. Federal research efforts should encourage

development of multi-sensor integrated measurement devices that are self-powered, interactive, and

intelligent for comprehensive self-learned/adaptive controls.


Gaps identified by

working group

Relevant research


Key metrics to be

addressed

Building/end-use

monitoring


deployment is limited

by cost

All Develop multi-tier metrics to balance

performance/cost trade-offs

Dramatically reduce cost for existing

performance and enable new lower-

cost sensors with reduced performance


Distribution PMUs

will play a key role in

distribution state

estimation and system

parameter correction

Research thrust 1:

High-resolution

building-to-grid

sensors.

Develop cost-effective distribution

PMU


Wireless, self-

powered, self-

calibrating sensors for

building efficiency are

needed

Research thrust 2:

High-accuracy and

low-cost building

efficiency sensors

Develop low-cost, wireless, self-

powered, self-calibrating sensors for

large-scale deployment


D-31

Gaps identified by

working group

Relevant research


Key metrics to be

addressed

Self-configuring and

self- commissioning

systems/equipment for

buildings are needed

Research thrust 2:

High-accuracy and

low-cost building

efficiency sensors

Develop auto self-configuration and

commission sensors


Multi-component

sensors for building

efficiency are needed

Research thrust 3:

Intelligent functions

for integrated

multi-component

sensors

Develop multi-component integrated,

low-cost sensors for building

efficiency


Closed loop and

transactive-based

control for building

efficiency and

distribution system

requested service,

such as load shedding

and var support are

needed

Research thrust 3:


for integrated

multi-component

sensors.

Develop multi-objective closed-loop

control across multiple systems


FDD and prognostics

are needed as part of

self-learning building

systems

Research thrust 3:


for integrated

multi-sensors.

Develop machine learning–based

building system–scale FDD and

prognostics for self-correcting controls



Based upon the results of the working group efforts and the associated gap analysis, three major research

thrusts were discussed and are highly recommended.

Research thrusts recommended

1. Development of high-resolution building-to-grid sensors (recommended)

2. Development of high-accuracy and low-cost building efficiency sensors (recommended)

3. Development of intelligent functions for integrated multi-sensors (recommended)



efforts supported under the GMLC and the GMI more broadly. Research focused on high-resolution

building-to-grid sensors (research thrust 1), as well as high-accuracy and low-cost building efficiency

sensors (research thrust 2) is being pursued under the GMLC Advanced Sensor Development Projects

(e.g., ultra PMU, optical sensors). The transactive energy program of the DOE Office of Electricity and

the transactive control program of DOE’s Office of Energy Efficiency and Renewable Energy have begun

supporting projects related to intelligent demand response and building controls (research thrust 3).

Despite the ongoing efforts in this area within the GMLC and GMI, clear opportunities exist to expand

upon the area of end-use/building monitoring to address the targeted research thrusts recommended by the

working group.

D-32

D.8 DISTRIBUTED COMMUNICATION ARCHITECTURE (LABORATORY LEADS: PETER

FUHR AND MARISSA MORALES-RODRIGUEZ, ORNL)


Distributed communication has been viewed as a promising solution to tackle the challenges from large-

scale deployment of distributed sensors in the future grid. This focus area targets architectural design for

distributed communication and an analysis of its impact on operation and control of the electric power

grid in terms of various applications. This working group reviewed the current proposed research thrusts

within this focus area of the Sensing and Measurement Technology Roadmap and developed a clear

understanding of the current industrial state of the art and quantitative metrics for new sensing and

measurement technology development.

The Distributed Communication Architecture (DCA) working group established a core group of

stakeholders spanning the DOE national laboratory system, academia, utilities, and vendors. The

following is a full list of participants.



Jim Glass Electric Power Board of Chattanooga [email protected]


Stuart Laval Duke Energy [email protected]

Nestor Camino Schneider Electric [email protected]

Sterling Rooke Brixon [email protected]

Yilu Liu University of Tennessee–CURENT [email protected]

Jeff Reed Virginia Tech [email protected]



Penny Chen Yokogawa Electric Company [email protected]

Peter Fuhr ORNL [email protected]

Marissa Morales-Rodriguez ORNL [email protected]

Of note is the interplay that occurred among a variety of sensor- and communication-related GMLC

working groups and the design of an integrated communication fabric that supports the operational

requirements associated with those groups’ activities. In parallel, there is need for a significantly

enhanced cybersecurity profile for the communication system. Discussions with DOE-sponsored cyber

researchers continue to examine the latest trends in security hardware and software intended for industrial

control systems in general and grid modernization specifically. Coupled with this cybersecurity focus is

an examination of the scale of realistically deployable network topologies in utilities of varying sizes and

sophistications, ranging from cooperatives (Flathead Electric Co-op, Lake Region Electric Co-op)

through municipally owned utilities (Electric Power Board of Chattanooga [EPB], Knoxville Utilities

Board), to larger utilities (Tennessee Valley Authority [TVA] and Duke Energy). Of further significance

is the role that the Industrial Internet of Things (IIoT) and distributed energy resources (DER) can

(inter)play in terms of sensing and control signals. The possibilities presented by both technology arenas

are noteworthy, as is the need for a communication fabric that may rely on more out-of-band signaling

than traditional supervisory control and data acquisition (SCADA) networks.











D-33

D.8.2 Working Group Activities

The DCA working group gathered a variety of communication architectures that vendors are proposing—

or have sold—to electric utilities specifically and energy delivery system end users in general. While

many such architectures are being promoted, there are four fundamental underpinnings to a next-

generation grid-centric distributed communication architecture that need to be addressed:

1. IIoT/IoT. The IIoT is a specialized IoT implemented in rugged packages suitable for industrial

application environments. In fact, legacy industrial control devices, such as programmable logic

controllers, will be compatible at least temporarily with the IIoT. The IIoT benefits from data flowing

through standard-based and common networks. From a networking standpoint, IIoT systems will

break the ongoing practice of using proprietary networks and bring into place a common standard-

based networking technology. The convergence of the IT technology and OT operations knowledge

for industrial automation environments is well under way. Soon, the IIoT will approach the network

edge for almost every industrial application. IIoT installations can include hundreds or even

thousands of sensors across a large facility. Numerous devices labeled IoT for home/building

automation cross the boundary with utility operations with varying levels of communication

technology and intersect with utility communication systems. Of special note are the waves of devices

that are directly IP-addressable32. This class of IoT/IIoT devices flattens the SCADA and SP95/ICS

(industrial control system) architectures (see Figure D.1). In-network, IP-addressable edge devices

place additional operational requirements on a utility’s intrusion detection system/intrusion

prevention system/unified threat management cybersecurity software system. Current IIoT/IoT

offerings to electric utilities and their associated communication and networking requirements33 are

being assessed in conjunction with EPB.

32 Such devices have received many labels, including “edge devices,” “end devices,” and “edge network

appliances.” Regardless of the name, they are directly IP addressable (as opposed to a network topology that has the

devices, sensors, and grid elements behind a gateway. 33 Networking topologies, core communication technologies, associated communication architectures.

D-34

Figure D.1. SCADA/ICS designs for networked control and automation system architectures as shown here rely on separation of functionality

and components. Many instrumentation standards, including SP88, SP95, and SP99, rely on such separations.

2. Wireless, frequency congestion (shared spectrum). Many sensors and systems identified in 1, as

well as their possible deployment within the utility grid of service systems, rely on wireless

communication. A significant portion of current wireless sensing/monitoring/control products being

offered to the utilities by large and small vendors rely on operations within the (license-free)

industrial, scientific and medical (ISM) frequency bands. Frequently, the information coming from

such sensors and systems is “offered with cloud capability,” meaning that some level of IT

connectivity is required. The (essentially) singular worldwide 2.4 GHz ISM frequency band exhibits

significant frequency congestion issues with WiFi, Bluetooth, and a wide array of proprietary

protocols all operating in that band. Anecdotal evidence provided by a few utilities—via discussions

with key personnel—reveals that utilities with many wireless devices, systems, and a communication

fabric deployed throughout their infrastructure are experiencing performance variation/degradation

due to this “spectrum crunch.”

On a related theme, 5G wireless is designed to have a very wide application space with three key

application domains: enhanced mobile broadband, massive machine-type communications, and ultra-

reliable and low-latency communications. The proposed network architectures for large- and small-

cell 5G infrastructure to support massive machine-type and ultra-reliable communications are key

features of NB-IoT,34 which will be deployed in 5G. Low latency (and very high) data rate

communication is the expectation. The 5G application space as envisioned by the International

Telecommunications Union (ITU) is shown in Figure D.2.

34 Where, for example, a simple 5G small-cell “system” may be established over a utility substation, thereby

providing wireless connectivity from devices to the 5G cellular server (not necessarily a telecommunications

company). The 5G server is integrated into the utility’s backbone core for delivery of sensor information to the

SCADA system via that core transport. Such a system architecture is being designed with and for EPB’s use of

wireless IIoT sensors and systems within their substations and on unmanned aerial vehicles (drones).

D-35

Figure D.2. Example applications as envisioned by the ITU for 5G.

The DCA working group leveraged the ongoing efforts of the Office of Science and Technology Policy

(OSTP) Networking and Information Technology Research and Development (NITRD) Wireless

Spectrum Research and Development group (WSRD) examining the use of shared spectrum to obviate the

spectrum crunch (Figures D.3 and D.4). Through the monthly WSRD meetings, as well as conferences

like the International Symposium Advanced Radio Technology (ISART 2017, Boulder, CO, August

2017) and the DOE-sponsored Interagency Spectrum Summit (DOE, Washington, DC, June 2017) the

DCA working group accumulated and distilled relevant information on current and future networking and

architectures and requirements.

Figure D.3. Frequency congestion showing continual requests for “more spectrum” and channel assignments for 802.11 and 802.15.4 compliant

transceivers in the 2.4 GHz band.

D-36

Figure D.4. Shared spectrum would require that additional intelligence/capabilities be programmed into advanced sensors with an associated

flexible communication network fabric.

3. Cybersecurity and cyber-physical security. Security of devices, sensors, control elements, and

related utility components is of paramount concern. In addition to the vendor information gathered,

the DCA working group reviewed best practice guides; DOE, NIST, and other recommended

architectures; network security functions and features; and DOE-OE’s DarkNet designs to present a

fall 2017 snapshot of cybersecurity and cyber-physical security activities most relevant to electric

utilities in general. Documents reviewed include those listed in Table D.1.

Table D.1. Various organizations have released—or offer—various communication architecture designs and

recommendations such as these.

4. Evaluation of various grid architectures. Activities concentrated on reviewing DER microgrid

designs and architectures including both AC and DC microgrid architectures (see Figure D.5).

Numerous meetings were held with a variety of complexity and capability utilities. One of these was

Organization, Program or

Activity

Re

co

gn

ize

d N

ati

on

al o

r In

tern

ati

on

al

Sta

nd

ard

s o

r R

eg

ula

tio

ns

Cre

ati

on

Bo

dy

Re

gu

late

s, M

on

ito

rs a

nd

En

forc

es

Imp

ose

s P

en

alt

ies

for

No

n-C

om

plian

ce

Mo

nit

ors

, In

spe

cts

, Lic

en

ses

an

d

Re

gu

late

s En

erg

y S

ecto

r P

roje

cts

Sta

nd

ard

s-B

ase

d D

ev

ice

Te

st &

Accre

dit

ati

on

Cyb

er

Se

cu

rity

Re

searc

h, T

est

,

De

ve

lop

me

nt

& V

alid

ati

on

Lab

ora

tory

Aw

are

ne

ss P

rog

ram

s

Cyb

ers

ecu

rity

In

cid

en

t R

esp

on

se

Mit

igati

on

an

d R

eco

ve

ry P

lan

nin

g

Vu

lne

rab

ilit

y A

sse

ssm

en

ts

Pe

rso

nn

el R

isk

Ass

ess

me

nt

&

Accre

dit

ati

on

Pe

rso

nn

el C

IP C

yb

er

Se

cu

rity

Tra

inin

g

Pe

rso

nn

el C

IP P

hysi

cal S

ecu

rity

Tra

inin

g

Pe

rso

nn

el C

IP O

pe

rati

on

al S

ecu

rity

(Ge

ne

ral)

Tra

inin

g

Re

searc

h f

or

Fu

ture

CIP

So

luti

on

s

En

erg

y S

up

ply

Ch

ain

Fo

cu

sed

Fu

nd

s In

de

pe

nd

en

t R

ese

arc

h

Pu

blicati

on

s P

ost

ed

on

Pu

blic o

r

Me

mb

ers

-On

ly W

eb

site

Pe

rio

dic

Ne

wsl

ett

er

to M

em

be

rs a

nd

/or

Ind

ust

ry

En

erg

y C

IP W

ork

forc

e D

ev

elo

pm

en

t

Pro

gra

ms

FERC

- Existing Capability X X X X X X X X X XNERC

- Existing Capability X IEC

- Existing Capability X X X X X X X ISA Secure

- Existing Capability X X X X X X Underwriters Laboratories

- Existing Capability X X X X X X X X NIST

- Existing Capability X X X X X X DOE/OE ES-C2M2 Cybersecurity

Capability Maturity Model

- Existing Capability X X X X X X X X X

Electricity Subsector Supply Chain Test, Training, Standards and Certification Organizations (Preliminary)

Features, Capabilities, Competencies, Responsibilities, and Authority - Related to Cybersecurity of the Electricity Subsector Supply Chain

D-37

a technical “deep dive” regarding EPB’s architecture for the implementation of GMLC advanced

sensors. This activity included investigations into network architecture(s) designed for cybersecure,

timely restoration of a DER microgrid-centric electric grid. This specifically examined the

communications architecture necessary for a sensor-laden, microgrid-centric architecture with a

variety of grid assets. The working group concentrated on a scalable sensing and control companion

communications network that may have subtle changes based on asset mixes.

Figure D.5. The cyber and connectivity architectures used at EPB. Source: EPB.


The team identified many key findings during the working group process, which informed the subsequent

recommendation about proposed research thrusts. A summary of the most significant identified gaps, the

proposed approaches to address the gaps, and their linkages to proposed research thrusts can be found in

the summary table in Section D.8.4. Key findings include the following:

1. Utilities, obviously, have a deployed communication network that supports their operations. Federal

research efforts on design and development of a cost-effective, scalable communications fabric to

support the wide range of next generation sensors, systems, and DER components are being

explored.

2. The IIoT and 5G wireless activities under way in the private, public, and academic sectors present an

array of concerns for electric utilities, including changes in the SCADA/ICS architecture,

cybersecurity vulnerabilities presented by the deployment of such devices, and use of “the Cloud” for

data archiving and operations. Federal research efforts to design a distributed communications

architecture that supports these technology developments are under way.

D-38


consequence events, such as man-made or natural events, to enable actions that can prevent large-

scale failures and minimize the impacts to increase grid resiliency. Federal research efforts should

target the development of a scalable, rapid, high-bandwidth and low-latency communications

network to support cybersecure transport electrical parameter measurements.



group

Relevant

research

thrust or

thrusts

Approach to address gap Key metrics to be

addressed

Current architectures are

inadequate for advanced security

and authentication protocols

(e.g., Open FMB, ICCP V2).

All Investigate array of sensors for utility

R&D and product development

activities. Identify throughput and

latency requirements for sensors

platforms (as opposed to individual

specific sensors)


Implications of varying IoT/IIoT

devices, sensors, and systems for

deployment throughout utility

networks (including residences)

All Develop compendium of IoT/IIoT

vendors’ and industrial groups’

recommended architectures

IoT attack surface

variation based on ad

hoc and at-scale

deployment of

IoT/IIoT

Spectrum congestion All Continue discussions with

OSTP/NITRD/ WSRD regarding

other agencies’ activities

Performance, latency,

bandwidth

5G cellular integration into

utility communication network

architecture

All Hold meetings with utilities (EPB,

Duke, TVA, Sempra, National Rural

Electric Cooperative Association)

regarding their future plans for

wireless sensors. Examine DarkNet

cyberphysical network topology for

applicability to GMLC. Continue

work with Virginia Tech

(Wireless@VT) program

5G integration points

within utilities’

existing cybersecurity

structure

Multiple data transport users on

a shared medium. Intertwined

communications performance

and cybersecurity across

differing layered network

topologies

All Reexamine the wide array of best

practice guides to industrial control

systems (ICS/SCADA). Update as

necessary, then vet with WSRD

Latency, reliability,

security (integration

into utility cyber

operations), ease of

utility use


Based upon the results of the working group efforts and the associated gap analysis, three different

research thrusts were developed for consideration and discussion. Of these research thrusts, all are being

recommended for prioritization. The full description of the research thrusts can be found in the document.

D-39

Research thrusts developed

1. Develop compendium of (principal) IT/OT network architectures (recommended)

2. Spectrum management, 5G, and cybersecurity (recommended)

3. Integration with multiple project sensor development and distribution grid asset working groups

(recommended)


Multiple projects involve developing sensors and systems with varying time scales and measurement

transport requirements. There needs to be a collation of these projects related to architecture needs and

communication backbone implications (wireless, wired, optical). The key measurement parameters are

latency, data throughput, multiple communication technology integration ported to utility network fabric,

and SCADA core.

D.9 COMMUNICATION AND NETWORKING TECHNOLOGY (LABORATORY LEAD:

CHEN CHEN, ANL)


Rapid development of new communication technologies in the communication community, especially the

IoT and 5G, present leveraging opportunities for grid modernization related to large-scale deployment of

distributed sensors. New networking technologies can also be used to address the challenges of

scalability, diverse quality of service (QoS) requirements, efficient network management, and reliability

and resilience. Another major challenge for the grid modernization effort is the interoperability among

diverse equipment and standards. This working group reviewed the current proposed research thrusts

within this focus area of the Roadmap and developed a clear understanding of the current industrial state

of the art and quantitative metrics for new sensing and measurement technology development.


The Communication Technology working group established a core roster of stakeholders spanning the

DOE national laboratory system, international research institutions, consultants, standardization entities,

and vendors. The following is a full list of participants.

Name Organization Contact Info



Bin Hu NIST [email protected]

Chen Chen ANL [email protected]


Larry Lackey Coergon (Consultant) [email protected]

Bob Heile Wi-Sun Alliance [email protected]

Di Shi Global Energy Interconnection

Research Institute North America

(GEIRINA)

[email protected]

Philip Top LLNL [email protected]










D-40



the initially proposed research thrusts within the Communication Technology area. The team was then

asked to critically review and comment on the initially proposed set of research thrusts developed by the

DOE national laboratory team and to provide insights into potential gaps that exist in terms of

communication technology within this area.

Based on team member input, a decision was made to refocus potential research thrusts based upon an

industry requirements orientation. The full list of the modified set of potential research thrusts to be

considered appears within the report. The team developed a set of quantitative metrics for communication

technology in sensing and measurement, including reliability, latency, scalability, security, ease of

deployment and further upgradability, cost-effectiveness, QoS, dynamic network services, and others.

These metrics provided a basis for the key findings outlined in this section, including the gap analysis

summary table.


The team identified many key findings during the working group process. A summary of the most

significant identified gaps, the proposed approaches to address the gaps, and their linkages to proposed

research thrusts appears in the summary table in Section D.9.4. Key findings include these:

1. There is a channel congestion challenge from current devices using scheduling mechanisms based on

fixed/deterministic/periodic or listen-before-talk schemes, and interference caused by operation of

non-interoperable devices. The challenge was created because most of the sensor-based solutions use

specific radios to communicate, as well as spectrum under-utilization. Federal research efforts

should (1) target distributed scheduling schemes that require distributed intelligence and common

communication paradigms for the network to operate autonomously and (2) use radios that can

support multiple technologies so that the devices can potentially get more information about the

type of data transfer.

2. Currently, not many IoT technologies can support 1 ms latency with >99% reliability to satisfy grid

applications. Federal research efforts should (1) identify needs and present requirements in the

standard body of emerging communications (e.g., 5G technique), (2) investigate distributed

intelligence to reduce information flow, (3) investigate key 5G techniques (e.g., ultra-dense

network, millimeter waves) and existing IoT-related techniques (e.g., machine-to-machine

communication, edge computing), and (4) identify the performance gap of those techniques for

smart grid communication.

3. There is a need to keep the development cost low while supporting future upgradability to newer

technologies. Federal research efforts should target development of an agnostic solution to the

communication technology supporting intelligent, autonomous, and cooperating devices.

4. The scalability issue should be addressed to enable networking of millions of nodes. Dynamic

resource allocation and controlling network features in runtime, and plug-and-play functionalities on

the device level, are necessary. Federal research efforts should investigate distributed intelligence

and architecture and develop a smart connectivity manager to enable various intelligent decision-

making (e.g., routing, channel condition aware, self-healing). Application development/resource

allocation needs to be done independently of communication technology.

5. Uncertainties and security risks caused by networking techniques should be considered. Federal

research efforts should quantify uncertainties and security risks in the smart grid context and

D-41

develop self-healing and more robust capabilities to oppose malicious operations (e.g., employ

cooperative security schemes to identify malicious operation/nodes).

6. Existing co-simulation platforms with integration and interoperability abilities should be leveraged.

Federal research efforts should leverage the existing platforms from the following aspects:

integrative, reconfigurable, reproducible, scalable, and usable.



group

Relevant research


Key metrics to be

addressed

Channel congestion challenges

from current devices using

scheduling mechanisms based on

fixed/deterministic/periodic or

listen-before-talk schemes

Research thrust 1:

Efficient spectrum

utilization with

interference management

Investigate distributed

scheduling schemes that require

distributed intelligence and

common communication

paradigms for the network to

operate autonomously

Reliability, spectrum

utilization,

throughput

Interference caused by operation of

non-interoperable devices, because

most of the sensor-based solutions

use specific radios to communicate

Research thrust 1:

Efficient spectrum

utilization with


Use machine learning at device

level to predict use of channels

by interfering devices.

Use radios that can support

multiple technologies so that a

device can potentially get more

information about data transfer

Interference

management to

acceptable signal-to-

noise ratios

Spectrum under-utilization for

smart grid applications

Research thrust 1:

Efficient spectrum

utilization with


Investigate how to define and

determine primary

users/applications and

secondary users/applications for

spectrum sharing techniques.

Investigate whether existing

spectrum sensing/sharing

techniques support diversified

performance requirements of

smart grid applications, as well

as performance needs of cyber-

physical systems that will co-

exist with smart grid.

Study how to increase the

spectrum utilization by sharing

the spectrum/network resources

across modern grid applications

and how to maximize the overall

network utility while satisfying

the performance requirements

for individual applications

Spectrum utilization

Not many IoT technologies can

support 1ms latency with >99%

reliability

Research thrust 2:

Leverage IoT

technologies in power

system communications

Identify needs and present

requirements in the standard

body of emerging

communications (e.g., 5G

technique).


intelligence to reduce

End-to-end latency,

reliability

D-42


group

Relevant research


Key metrics to be

addressed

information flow.

Investigate key 5G techniques

(e.g., ultra-dense network,

millimeter waves) and existing

IoT-related techniques (e.g.,

machine-to-machine

communication, edge

computing) and identify the

performance gap of those

techniques for smart grid

communication

Keep the development cost low and

support future upgradability of

newer technologies.

Research thrust 3: Cost-

effectiveness analysis of

deploying new

communication

technologies

Develop a solution-agnostic

solution to the communication

technology for supporting

intelligent, autonomous and

cooperating devices

Ease of deployment

and future

upgradability, cost-

effectiveness

Address the scalability issue Research thrust 3: Cost-

effectiveness analysis of

deploying new

communication

technologies

Research thrust 4:

Networking technologies

for scalability issue while

satisfying diverse QoS

requirements.


intelligence and architecture.

Development of smart

connectivity manager to enable

various intelligence decision-

making (e.g., routing, channel

condition aware, self-healing).

Application development

/resource allocation needs to be

done independently of the

communication technology

Scalability, QoS

support

Dynamic resource allocation and

controlling network features in

runtime, and plug-and-play

functionalities on device level

Research thrust 5:

Efficient network

management to support

new and dynamic

services

Develop smart connectivity

manager and enable a radio-

agnostic design of applications

Supporting dynamic

network services,

supporting adaptive

scheduling and

resource allocation

The uncertainties and security risks

caused by networking techniques

Research thrust 2:

Leverage IoT

technologies in power

system communications.

Research thrust 6:

Reliability and resilience

enabled by networking

technologies.

Research thrust 7:

Identification of

requirements and use

cases from sensing and

measurement perspective

Quantify the uncertainties and

security risks in the smart grid

context

Employ dynamic routing

enabled by smart connectivity

manager

Employ smart connectivity

manager to enable self-healing

and more robustness against

malicious operations (can

employ cooperative security

schemes to identify malicious

operation/nodes)

Reliability,

resilience, security

Use case identification for Open

FMB from sensors perspective

Research thrust 7:

Identification of



Clustering of use cases based on

sensors and/or requirements will

be helpful

Comprehensive list

of use cases and

requirements in

sensing and

D-43


group

Relevant research


Key metrics to be

addressed

measurement perspective measurement of

smart grids

How to choose networking

technology to use to forward data

(when many options are available)

in OpenFMB

Research thrust 7:

Identification of



measurement perspective

A smart connectivity manager

makes the network intelligent

and autonomous. This layer can

also be a subset of the

OpenFMB interface layer

Message

exchangeability and

message conformity

to the standards

Leverage existing co-simulation

platforms with integration and

interoperability abilities

Research thrust 8: Large-

scale co-simulation of

cyber-physical system

integrating

interoperability solution

Leverage the existing platforms

from the following aspects:

integrative, reconfigurable and

reproducible, scalable, and

usable

Heterogeneous

hardware such as

fiber, copper, power

line carrier, mesh

networks, point-to-

point radios, LTE

cellular; maybe in

the future add GHz

cellular, suitability

for the distributed

architecture,

adaptability to use

cases regarding

sensing and

measurements


Based upon the results of the working group efforts and the associated gap analysis, a total of eight

different research thrusts were developed for consideration and discussion. To minimize overlap with

other focus areas, this number was condensed and reduced to five different research thrusts. Of these

research thrusts, four were recommended for prioritization.

Research thrusts developed (initial):

1. Efficient spectrum utilization with interference management

2. Leverage IoT technologies in power system communication

3. Cost-effectiveness analysis of deploying new communication technologies

4. Networking technologies to address scalability issue while satisfying diverse QoS requirements

5. Efficient network management to support new and dynamic services

6. Reliability and resilience enabled by networking technologies

7. Identification of requirements and use cases from sensing and measurement perspective

8. Large-scale co-simulation of cyber-physical system integrating interoperability solution

Research thrusts developed (after combining with other working groups):

1) Leverage IoT technologies in power system communication (recommended)

2) Networking technologies for scalability issue while satisfying diverse QoS requirements

(recommended)

3) Efficient network management to support new and dynamic services

D-44

4) Reliability and resilience enabled by networking technologies (recommended)

5) Large-scale co-simulation of cyber-physical system integrating interoperability solution

(recommended)



efforts supported under the GMLC and the GMI more broadly. Research focused on large-scale co-

simulation of cyber-physical system integrating interoperability solution (research thrust 8) is being

pursued under the GMLC project Development of Integrated Transmission, Distribution, and

Communication (TDC) Models, which integrates simulators designed for separate TDC domains to

simulate regional and interconnection-scale power system behaviors at unprecedented levels of detail and

speed. Another related project is the GMLC CyDer project, Cyber Physical Co-Simulation Platform for

Distributed Energy Resources in Smart Grid, which develops a modular and scalable tool combining

transmission and distribution system simulation, data collection and analysis, power generation and load

forecasting, load flexibility, and real-time control of solar PV. Despite the ongoing efforts in this area

within the GMLC and GMI, a clear opportunity exists to expand upon the area of communication

technology to address the targeted research thrusts recommended by the working group.

D.10 DATA ANALYTICS (LABORATORY LEAD: EMMA STEWART, LLNL)


Evaluation and maintenance of grid health currently depends on a centralized, deterministic approach in

which data are collected and analyzed, and some control action is then taken. In contrast to traditional

centralized grid data monitoring and analysis, building component health relies on a decentralized

analytic approach in which each building component is monitored and analyzed individually. Mere

availability of more data will not, by itself, lead to changes in grid visibility, security, and resiliency. To

create the predictive and prescriptive environment required to enable new markets and transactions for

customer revenue and a reliable grid, the data must be collected, organized, evaluated, and analyzed using

sophisticated pattern detection (i.e., incipient failure analysis can have subtle signatures recognizable only

by advanced analytics). Discovery algorithms can provide actionable information, allowing operators and

customers to reliably manage an increasingly complex grid.

This working group reviewed the current proposed research thrusts in the sensing and measurement focus

area and developed a clear understanding of the current industrial state of the art and quantitative metrics

for new analytics development along with the backbone of new sensors.


The following is a full list of participants.


Alireza Shahsavari University of CA–Riverside [email protected]

Gordon Mathews University of CA–Riverside [email protected]

Jim Glass Electric Power Board of

Chattanooga

[email protected]

Jouni Peppanen EPRI [email protected]





D-45


Junbo Zhao NASPI Distribution Task

Team/PNNL

[email protected]

Lilian Bruce Electric Power Board of

Chattanooga

[email protected]

Lingwei Zhan ORNL [email protected]

Mahendra Patel EPRI/ formerly with PJM [email protected]

Reza Arghandeh FSU–Center for Advanced

Power Systems

[email protected]

Sascha von Meier NASPI Distribution Task

Team

[email protected]



Neelofar Anjum PG&E [email protected]

Lillie Alvarez RPU [email protected]

Venkat Shastri USD [email protected]

Sean Murphy Ping Things [email protected]


Review and Assessment Report and was provided existing material developed by the project national

laboratory team.

Based on team member input, a decision was made to refocus potential research thrusts around metrics

for future analytics and a gap analysis of the existing spread of analytics as it pertains to the field of

electric grid analysis. The team established a matrix and relevant metrics within the matrix, spanning grid

levels and data analysis types. The data distribution levels considered were locally distributed and

centralized, and the time frames of analytics were past, present, and forward-looking. A survey of the

existing and future state-of-the-art of data analysis was considered in creating the metrics. Vendors were

specifically requested to provide input.


The team identified two key areas during the working group process that led to the proposed research

thrusts. A summary of the most significant identified gaps, the proposed approaches to address the gaps,

and their linkages to proposed research thrusts appear in the summary table in Section D.10.4. Key

findings include these:

1. Data preparation is a key limitation for data analysis and should be considered as a key gap within

data analytics, rather than the analytics themselves.

2. Multimodal and multivariate analyses, integrating new sensing types and considering synchronization

and reconciliation of these data sets, would be a valuable contribution.












D-46


Gaps identified by

working group

Relevant research thrust or

thrusts Approach to address gap

Key metrics to be

addressed

Grid asset monitoring


deployment is limited by

cost

All Develop analytics to leverage

new data sources efficiently


Non-localized signatures of

failures or faults are difficult

to detect with individual

sensors

Research thrust 2:

Multimodal multivariate

algorithms

Develop analytics that use

disparate data sources for fault

location and identification

Areal or linear spatial

resolution,

performance, cost

Electrical parameters can

provide the most rapid

signatures of low-

probability, high-

consequence events, such as

human or natural threats

(e.g., geomagnetic

disturbance, electromagnetic

pulse)

All Deploy analytics with existing

and emerging electrical

parameter measurements

Performance, latency,

bandwidth and cost

Data-driven analysis is

siloed by sensor and data

type and does not leverage

the full range of data

available for maximal

efficiency and lowest cost

Research thrust 2:

Multimodal multivariate

algorithm development

Present use cases in a multi-

sensor domain. Develop and

demonstrate multimodal,

multivariate machine learning

techniques for real-time and

predictive analysis of a wide

range of grid conditions, as

presented in the use cases

Reliability,

correctness, cost,

accuracy, data

acquisition latency,

computational budget,

precision, scalability

Data quality from new and

existing sensors drives the

application and usefulness

of the algorithms and is a

critical issue. This issue is

often considered solved by

industry, but it returns as a

critical issue, often after

deployment

Research thrust 1:

Data preparation (validation,

quality assessment,

conditioning/correction)

Develop consistent metrics and

methodology to evaluate the

impact of data quality on the

range of algorithms across the

grid and analytics domains

Latency, reliability,

correctness, cost


Based upon the results of the working group efforts and the associated gap analysis, two prioritized

research thrusts have been developed for consideration and discussion.


1) Data preparation (validation, quality assessment, conditioning/correction)

2) Multimodal multivariate algorithms



efforts supported under the GMLC and, more broadly, the GMI with research focused on distributed

D-47

analytics (1.4.9) and under projects within DOE SETO (VADER, CyDER). Despite the ongoing efforts in

this area within the GMLC and GMI, a clear opportunity exists to expand upon the area of data analytics

to address the targeted research thrusts recommended by the working group.

D.11 DATA MANAGEMENT (LABORATORY LEAD: PHILIP TOP, LLNL)


The power grid is becoming more highly networked as it transitions to a modern power system with key

features such as two-way power flow. Because of this high degree of connectivity, there is a significant

increase in both the volume and variety of data being created to monitor and control the system. These

data represent a significant opportunity for existing and future applications that can intelligently operate

on such a diverse data set; but for these applications to be successful, the data must be maintained in a

coherent fashion. Two key challenges in this area are access to data and the data organization. Efficient

and accurate data management systems must be in place to ensure that the data are distributed where

needed in an on-time and reliable fashion, and the results are consistent and accurate. This working group

reviewed the current proposed research thrusts within the relevant sensing and measurement technology

roadmap focus areas and developed a clear understanding of the current industrial state of the art and

quantitative metrics for new sensing and measurement technology development.


The Data Management working group established a group of stakeholders who have expertise and are

interested in various components of the topic area. The following is a list of participants.



Lingwei Zhan ORNL [email protected]

Mahendra Patel PJM [email protected]

Junbo Zhao Virginia Tech [email protected]

Alireza Shahsavari University of CA–Riverside [email protected]



Philip Top LLNL [email protected]

Chen Chen ANL [email protected]


YC Zhang NREL [email protected]


Review and Assessment Report and thrust areas to become familiar with the current state of the art used

in developing the initially proposed research thrusts within the Data Management area. The team was then

asked to provide insights into potential gaps that exist in research and available technology and metrics

for different topics areas. Team members were asked to identify not only gaps in the technology but also

gaps in existing research to identify areas where additional research effort was warranted and would have

the most impact.












D-48


The team identified many key findings through subsequent discussions during the working group process,

which resulted in the following recommendations concerning the data management area. What became

clear through the process was that distinct metrics between evaluating the output of thrust areas and the

prioritization of thrust areas were needed. Key findings include the following:

1. Much R&D is occurring at many institutions—commercial, educational, and government-

sponsored—regarding data management and various technologies for dealing with data. Numerous

technologies of various kinds were noted in the working group process. However, very little is

making its way into power grid operation for three identified reasons:

a. Because there is no well-accepted way to quantify the benefits of data management technology,

there is no way to justify the expense (initial and ongoing) to regulatory bodies and other

stakeholders.

b. There is an educational gap in the electric utility space regarding data management practices and

simple techniques and, in these institutions, there is a lack of the knowledge required to

implement and maintain many potential solutions.

c. There are so many different quickly changing options for different technologies that in a slow-

moving industry, it is impossible for a utility to keep up and maintain operations adequately.

Some aspects are new and lack standards, and others have many competing standards. The effect

is the same: there is no easy solution, so no solution is chosen.

Federal research efforts in data management in the utility sector should specifically focus on

addressing these three gaps: cost justification, workforce education, and standardization.

2. It became evident in speaking with representatives of utilities and operators that one reason why

operators are not using more advanced data and analytics for management of the grid is that the

displays and indicators are not usable in the context of a grid control room. The displays and

indicators too frequently require advanced understanding and in-depth study to understand and use.

Plus, a utility operator needs an actionable decision from these data. This is reflective of the

disconnect between researchers and operators about how humans operate in the control room

environment. Federal research efforts in data management for visibility should focus on human-

machine interactions with visualization. In addition, efforts to include operators much earlier in

the development process and in partnership with researchers would be of great benefit to both

sides.


Gaps identified by

working group


or thrusts Approach to address fap

Key metrics to be

addressed

There are no standard

reliable and defensible

ways to evaluate the value

of data management

systems to justify the initial

and ongoing costs

? Define a well-justified

standardized way of

determining the benefit

gained from data

management systems and

technologies

Cost

justification

D-49

Gaps identified by

working group


or thrusts Approach to address fap

Key metrics to be

addressed

The data formats and

standards used in collecting

sensor data are not well

accepted or interoperable

1,3 Establish best practice

guidelines and testing

measures for data

management systems and

develop data use standards

per a GMLC consortium

Interoperability,

cost,

ease of maintenance

There is a lack of qualified

personnel at many utilities

to manage a complex data

management system

? Standardize tools and

curriculum at the university

level

Make training courses

easily available to the

current workforce

Reduce system complexity

Ease of use,

cost

Compiled data and

advanced analytics are not

used or usable by operators

2 Establish collaboration

between grid operators and

researchers on displays and

human machine interfaces

to develop guidelines and

standards for future

displays and interfaces

Ease of use

Data are frequently siloed

and not accessible by

analytic tools that could

make use of it

4, 5 Establish best practices,

along with tools and

technologies, for managing

and interfacing large

disparate data sets.

Establish standards and

technologies for

appropriately distributing

the data

Interoperability,

Security,

Extensibility,

ease of use,

cost

Proposed Research Thrusts and Prioritization

Six research thrust areas were developed for consideration and discussion through the Roadmap

development, along with three identified gaps or challenges that need to be addressed. Of these research

thrusts, two are being recommended for prioritization as indicated. Descriptions of the research thrusts

can be found in the document.


1. Data collection

2. Visualization and human interfaces (recommended)

3. Data access and interfaces (recommended)

4. Data organization

5. Data distribution

6. Online monitoring of distributed algorithms

D-50

Overall gaps:

1. Benefits quantification and justification

2. Lack of workforce

3. Standardization and long-term support


In some ways, the identified thrusts are research areas that are not well addressed by other GMLC

projects or external entities, because that was one of the metrics for the gap analysis. Many of the topic

areas covered under data management are being addressed in part in many of the projects throughout the

GMLC. The Data Analytics project (1.4.9) interacts heavily with several thrust areas. In addition, many

projects like Advanced Sensors Development, Data Analytics, and other projects related to modeling have

a vested interest in standardization of interfaces and data access technologies. In a broad sense, the

development of standards should be done as a consortium rather than through individual competing

institutions. A standard developed by one or more institutions will not gain sufficient traction to have a

rapid impact, whereas a standard developed by representatives from many labs and industry members

might gain much faster acceptance. The GMLC can provide a framework to accomplish that aim.

E-1

APPENDIX E. USE CASES

E.1 USE CASE: FAULT DETECTION, INTERRUPTION AND SYSTEM RESTORATION

Objective:

Identify the optimal number and locations of fault detection and system restoration devices.

Description:

The protection systems for distribution systems were designed to detect and isolate faults locally and

quickly to minimize the number of consumers impacted by faults. Fuses are used closest to consumer

loads to protect consumer equipment from high fault current. The fault is isolated by the operation of the

fuses (“melts”) closest to the fault. They provide an isolation measure for permanent faults, while

reclosers at the distribution substation and along distribution circuits protect the circuits, circuit, or a

section of the circuit and provide an isolation measure for both temporary and permanent faults. If the

fault on the circuit is temporary, such as a tree limb brushing against an energized line, then the recloser

should be able to clear the fault after one or two recloser operations (deenergize then reenergize the

circuit during each operation). However, if the fault on the circuit is permanent, then the recloser stops

trying to reclose into the faulted line after a set number of operations (e.g., two or three), and the faulted

section is isolated from the rest of the circuit, allowing the unfaulted section of the circuit now isolated

from the faulted section to be reenergized by the distribution circuit breaker or an upstream recloser.

Since distribution systems were designed for one-way power flow, the introduction of distributed energy

resources (DER) has an impact on existing protection approaches and systems.

The operation and protection of electric distribution systems is becoming more complex with the

deployment of DER, energy storage, and responsive customer loads. The introduction and continued

deployment of DER and storage introduces bi-directional power flow on these distribution circuits, and

responsive customer loads change the demand of customer loads in response to utility signals or pricing.

Thus, distributed resources can impact voltage profiles along with current flows on distribution

systems/circuits and thereby impact protection devices and settings by their presence.

Fault detection, interruption and system restoration devices, such as the S&C Intellirupter, or other novel

sensors and transducers, such as those being developed through the GMLC projects that employ both

switchgear and control logic, are being deployed to provide more reliable power to distribution systems.

They provide a means of rapidly detecting and interrupting faults and providing system restoration using

intelligent detection and control logic and fault isolation switchgear. In the case of a temporary fault, the

Intellirupter has only to detect and interrupt the fault until it dissipates and thus does not have to change

the current substation feed for the distribution loads. However, in the case of a permanent fault, the

Intellirupter not only detects and interrupts the fault but also may change the substation feed for loads

downstream of the fault. There is no existing methodology for the deployment of these devices—only the

rule-of-thumb methods used by engineers, such as the placement of two or more of these devices along

distribution circuits. Plus, there isn’t an existing methodology for their placement to account for various

levels of DER in the system, which can impact protection device locations and settings.

Research Objectives:

This proposed use case targets the following research objectives:

E-2

1. Develop a sensor optimization placement framework and tool for determining how fault detection,

interruption, and system restoration devices, such as Intellirupters, should be placed to achieve

optimal reliability performance on distribution systems both with and without DER.

2. Demonstrate this methodology using the electric power system model and other data from utility

partners (e.g., Electric Power Board of Chattanooga [EPB], which has a fiber optics communication

backbone in place from its earlier Smart Grid Investment Grant project).

Relationship with Proposed Focus Areas and Research Thrusts:

Detecting and interrupting faults and the restoration of the power system are key to ensuring the reliability

of smart distribution systems. The evolution of the smart grid, with the deployment of high penetration

levels of DER, makes it more complex to maintain the high level of reliability that power systems

currently have. Thus, it is critical that protection devices be both properly and adequately placed and that

they allow for the adjustment of protection settings as the state of distribution circuits varies with the

changing status of the DER on the circuit. Widespread deployment of these devices also requires

advances in distributed communication architectures and efficient data management.

E.2 USE CASE: INCIPIENT FAILURE DETECTION IN ELECTRICAL GRID ASSETS

Description:

A common emerging theme throughout the development of the sensing and measurement technology

roadmap has been the need for new sensing and measurement technologies that enable the detection and

identification of incipient failures within the electrical grid infrastructure. The ultimate objective of early

detection schemes is to provide utilities and other stakeholders with sufficient warning time and

specificity regarding the failure mechanism to enable condition-based maintenance responses that prevent

potentially disruptive, costly, and even catastrophic failures before they occur. A prominent example of

critical grid assets for which incipient failure detection has a clear value proposition is large power

transformers. Catastrophic transformer failures have large direct economic and social costs as well as

major opportunity costs because of the long replacement times for these custom, bulky components with a

highly constrained domestic manufacturing supply chain. For this reason, a range of commercially

available sensors and diagnostics tools and methodologies have been successfully developed for both

online and offline monitoring of large power transformers. However, the associated costs of existing

commercial systems limit their deployment to power transformers large enough that the potential

economic costs to the utility outweigh the costs of system installation, maintenance, and operation.

There is a clear value proposition for specific monitoring and measurement of the condition of large

power transformers through techniques such as dissolved gas analysis. But distribution asset monitoring

does not benefit from the economies of scale in the same way. Each component is a magnitude smaller at

least; and for every large power transformer, there may be thousands of distribution-level transformers. At

present, condition monitoring and maintenance in the distribution system is based upon a run-to-failure

and age-based approach. Often, the first sign of a distribution transformer failure is an outage for a

number of customers, detected via smart metering, or a customer call to indicate a component with a

visible failure (e.g., smoke).

Emerging needs exist for new sensing and measurement technologies spanning devices, communications,

and analytics to enable the successful realization of incipient failure detection schemes. There is also a

need for associated condition-based maintenance programs ubiquitously throughout the electrical grid

infrastructure, including but not limited to distribution systems and distribution-level assets. An increased

reliance on advanced data analytics methodologies, as well as the development of low-cost,

E-3

multifunctional sensor devices compatible with deployment in electrical systems and assets, will play a

key role in successfully realizing this objective.


This proposed use case targets two primary research objectives:

1. Develop and demonstrate novel data analytics methodologies that leverage existing and new sensing

and measurement technologies for incipient failure detection at lower cost and higher fidelity than is

currently possible with traditional large power transformer monitoring.

2. Develop and deploy new low-cost multifunctional sensor devices at a sufficiently low price point for

incipient failure detection for distribution transformers and other grid assets such as energy storage

devices. Existing sensing and diagnostic techniques are not yet widely deployed for these.


Successful realization of widespread implementation of incipient failure detection schemes for electrical

grid assets interfaces with a number of focus areas and research thrusts identified in the Roadmap. These

span the areas of new sensor device development for asset health and functional performance monitoring,

as well as advanced data analytics tool development and applications. Widespread deployment of low-

cost sensor devices and data analytics algorithms will also require advances in distributed communication

architectures and efficient management of large quantities of data in distributed network architectures.

E.3 USE CASE: SENSING AND MEASUREMENT TECHNOLOGY TO MITIGATE AGAINST

IMPACTS OF CYBER OR MAN-MADE ATTACKS

Description:

Cyberattacks on critical infrastructure are increasing in number. Although most of the attacks have

targeted only business networks, attacks on the Ukraine power grid in 2015 and 2016 demonstrated the

reality of cyber-physical attacks on the grid resulting in load loss and widespread outages. Although no

such attacks on the US grid have been successful yet, there is clear need to develop capabilities for timely

cyber attack detection and mitigation.

A significant amount of both industry and government R&D has been invested in protecting the

electricity transmission system. However, transmission substations are typically owned by utilities and

have few or no constraints on cost, bandwidth, computing power, and quantities of data that can be

collected and processed. On the other hand, given the high penetration of DER, and the proliferation of

home automation and Internet of Things (IoT) devices, the distribution network is particularly vulnerable

to cyberattacks. The attack surface is vast, and frequently security is not considered as part of the

deployment and design of automation and IoT devices. The number of vendors providing the devices is

very large, compared with the transmission system where a utility has control. The protection of these

devices is typically left to the individual owners, creating an easy entry point for cyberattack vectors that

can then propagate through these systems and cause upstream cascading effects. To contrast with

transmission systems, DER have strict cost, bandwidth, computing, and data storage constraints. This

situation drives a need to develop new sensing and analytics capabilities that will be specifically tailored

to distribution systems. The main objective of these capabilities is to detect, isolate, and mitigate

cyberattacks in early stages, before they propagate through the network or cause significant impacts to the

larger system.

E-4

The development of new capabilities is necessary to understand what data are useful in enabling the

detection of cyberattacks, understanding which parts of the system are and will be affected, and

understanding how to best isolate and/or mitigate attacks. Ideally, these capabilities would be agnostic to

the type of device or vendor and would have low overhead on the existing devices.


The proposed use case has the following objectives:

1. Develop and demonstrate low-cost sensing and analytics capabilities that will enable timely

cyberattack detection on distribution systems before large-scale attack propagation and impacts occur.

2. Develop analytics capabilities able to distinguish between faults resulting from cyberattacks and

regularly occurring faults.


The use case interfaces with a number of focus areas and research thrusts identified in the Roadmap,

including sensor devices, communications, and data management and analytics. It also involves the

optimal sensor placement identified.

E.4 USE CASE: INTEGRATING ADVANCED RESOURCE FORECASTS FOR

TRANSMISSION AND DISTRIBUTION GRID OPERATION

Objective:

Grid integration of advanced forecasts of variable renewable resources, including at the grid edge, for

enhanced observability, lean reserve procurements in market operations, and improved grid flexibility.

Description:

Power system decision support tools, including market dispatch tools, energy management systems, and

distribution management systems, need high-fidelity power forecasts under future scenarios with

increasingly variable renewables. Currently, industry uses include forecasts on an hourly basis for day-

ahead operation and 5 minute levels for real-time operation. But much needs to be achieved in terms of

using the uncertainty information of the mean forecasts (such as probabilistic forecasts), which can be

extremely valuable in forecasting net-load uncertainties and, consequently, reserves and ramping product

requirements in day-ahead and real-time operation. Additionally, current spinning and nonspinning

reserves procurement for contingencies uses exante preventive planning. But having low-latency, highly

accurate real-time forecasts will enable the procurement of reserves as a corrective paradigm using the

latest forecasts after a severe contingency event. This will allow for further reduction in reserves

procurement and the related costs.

Integrating advanced forecasts into market operations will also pave the way for using variable

renewables for grid flexibility. Generally, flexible conventional generation, such as gas units, is thought

of as a solution to mitigate the uncertainties caused by variable (in terms of power output) renewables.

But highly accurate, low-latency forecasts can enable renewables to be part of the solution for flexibility

rather than a problem.

Another challenge is to integrate better forecasts of grid-edge solar resources and, consequently, to

estimate the net load at the feeder head accurately. This will enhance the visibility of the grid states for

E-5

the purposes of better distribution grid management, accurate fault identification, and voltage control, as

well as economic procurement of reserves to mitigate uncertainties arising from the distributed solar

photovoltaic (PV) resources.


Demonstrations are needed to show the impact and value proposition of advanced forecast integration into

independent system operator and utility operations. Synergistic coordination of variable renewables with

demand response and storage technologies can be studied from such a use case demonstration. This use

case targets not just the distribution grid but the entire transmission and distribution grid for efficient

integration of weather sensing devices and their data. As more integration efforts are carried out across

the continental United States, there will be more need for disparate high-resolution weather data access,

data quality control, and standardization.

The value of this use case can be estimated in terms of grid economics, flexibility, reliability, and

resilience under extreme events. Additionally, the synergies between weather and grid sensors can be

studied to explore the value of replacing other expensive grid sensors, such as phasor measurement units,

with the available low-cost weather sensing systems and their forecasts.

E.5 USE CASE: TOPOLOGY DETECTION WITHIN THE DISTRIBUTION SYSTEM

Description:

This use case considers sensing, measurement, and analytics technologies that enable the detection,

reconstruction, and identification of topology within the electrical grid infrastructure. Topology in itself

deals with the configuration, phase, and status of switches, loads, breakers, and substations.

As customer-side technologies become a common part of the grid landscape, and distributed controls

become prevalent, it is becoming critical for utilities to understand the electrical connectivity of

components to ensure that sufficient visibility and control can be maintained over these highly dispersed

variable components. Ubiquitous sensing and measurement in themselves are not sufficient to identify all

potential configurations without the coupling of analytics and interpretative technologies.

Topology identification will provide utilities with an accurate picture of the configuration of the grid and

the load that it serves at any given time. Typically, at the distribution level, topology is corrected or

analyzed through the existing utility geographical information system (GIS). From the geographical

models, electrical model updates are extracted, which should account for the most recent changes to the

power system. Herein lies the difficulty of ensuring an accurate representation of system changes.

Without direct sensing and measurement of a particular topology change or manual input of a change due

to switching operations, the system model will be inherently error prone. Typical methods of GIS

correction include manual inspection—a time-consuming, and often impossible process, especially in

urban areas or in an automated system scenario. The building level-topology is often completely unknown

to the utility, and the low-voltage network is not commonly modeled in the GIS.

Topology can be identified through specific sensing and measurement on each switch or device capable

of changing the topology. In high-voltage transmission scenarios where switching is automated, this is

essential for control. At the distribution and building levels, there is presently little cost benefit to

individual sensing of all topologies. In addition, the sensing of each topology change at these two levels

in itself does not provide a reconstructed singular picture of topology. Sensing must be parsed with

analytics to enable full visualization to be realized. Building controls and advanced electric distribution

management system functions with DER require accurate and reliable distribution system modeling,

E-6

monitoring, and coordination. Topology reconstruction and learning of electrical connectivity will enable

accurate, local service provision.

Learning the topology of the distribution grid from measurements is an essential precursor for multiple

distributed tasks related to economic activities of aggregators, as well as safety monitoring and distributed

control to guarantee safe operations. A key to grid resilience is knowing what resources are available and

where those resources are electrically with reference to grid topology. This process requires a multi-

redundant, robust, decentralized approach. Decentralization with links to a higher-level hierarchy is key to

a fast recovery. Learning of grid topology requires that systems be modeled on a time scale. Machine

learning–based analytics would support each area of grid modernization by using this growing volume of

data to improve the detection of normally invisible phenomena, learn grid topology, and support security

applications, including detection of physical or cyber-based attacks.


The proposed use case targets two primary research objectives:

1. Develop and demonstrate novel data analytics methodologies that leverage existing sensing and

measurement technologies for accurate topology detection.

2. Integrate advanced analytics with simulation and utility and building advanced distribution

management systems.


The successful realization of widespread implementation of topology identification schemes for electrical

grid assets interfaces with a number of focus areas and research thrusts identified in the Roadmap for

advanced data analytics tool development and applications. Widespread deployment of low-cost sensor

devices and data analytics algorithms will also require advances in distributed communication

architectures and efficient management of large quantities of data in distributed network architectures.

Solving these problems practically (designing scalable algorithms) will require trade-offs among many

elements. These include complete vs. model-reduced (coarse-grained) descriptions, centralized vs.

distributed approaches in terms of both measurements and controls, and physics-intense (equation-based)

and physics-blind (equation-free) machine learning (inverse problems) approaches and techniques. Useful

topology identification requires the development of practical solutions and compromises for placing

measurement and control devices and storing and using the appropriate amount of data.

E.6 USE CASE: SENSING AND MEASUREMENT TECHNOLOGY TO MITIGATE AGAINST

IMPACTS OF NATURAL DISASTERS AND ENHANCE GRID RESILIENCE

Description:

Recent severe power outages caused by extreme weather hazards have highlighted the importance and

urgency of improving the resilience of the electric power grid. For example, Superstorm Sandy in 2012

left more than 8 million customers without power across 15 states and Washington DC on the east coast

of the United States. It is estimated that the inflation-adjusted cost of weather-related outages in the

United States is $25 to $70 billion annually. On the one hand, the current electric distribution grids

remain vulnerable to extreme weather events. On the other hand, customers’ expectations for the

continuity of electricity services have increased with the evolution of modern society’s reliance on

E-7

electricity. To enhance grid resiliency against natural disasters, the power industry focuses on improving

the distribution system restoration in a more quick and efficient way.

One big challenge for distribution system restoration in natural disasters is the lack of situational

awareness regarding the damage status of the distribution grid. The current practice still mostly relies on

damage assessors to patrol the feeders to identify trouble spots and evaluate the extent of damage, which

is a very slow process and is based on which restoration efforts can be coordinated. In addition, most

current distribution systems are “blind” in terms of monitoring and control capability beyond the

distribution substation. Even if some observability is enabled by automated meter reading information or

distribution automation, measurement data after a natural disaster may be unavailable or questionable

because the devices as well as the underlying communication network may also be damaged. To pinpoint

the faulted areas, the current outage management systems usually depend on customer trouble calls,

which are slow and inaccurate. Furthermore, the data silos among different data sources impact the ability

to achieve situational awareness in a timely manner.

The development of sensing and measurement technology has the potential to improve the situational

awareness of the grid before and after natural disasters and thus can improve the distribution restoration

practice for utilities. For example, from the device-level perspective, the development of low-cost sensors

to monitor asset statuses (e.g., via asset monitoring sensors) as well as grid condition (e.g., smart meters,

phasor measurement units, distribution automation sensors) could provide additional vision for estimating

damage status and increase redundancy to achieve observability under severe conditions. From the

communication-level perspective, the development of a distributed communication architecture as well as

an associated self-healing mechanism could achieve resilient communication to mitigate the impact of

infrastructure damage due to natural disasters. From the data management and analytics perspective, the

development of advanced data management techniques could enable the efficient integration of multiple

data sources from different types of sensors to improve grid situational awareness. The development of

data analytics methods to estimate the damage status could be robust against missing or erroneous

measurement data due to the impact of natural disasters.


This proposed use case targets research objectives as follows:

1. Develop and deploy low-cost sensor devices that could provide observability of asset statuses as well

as grid condition. Their ability to withstand the disaster would be an advantage.

2. Develop optimal sensor placement strategies to ensure a certain level of redundancy for observability

under severe conditions.

3. Develop distributed communication architectures with functionality that does not rely on

infrastructure availability and can provide dynamic networking features, which are resilient to natural

disasters.

4. Develop a self-healing mechanism to recover a certain level of communication to mitigate the impact

of damages.

5. Develop data management schemes to achieve efficient integration of multiple sources of sensor

information to enhance damage assessment

6. Develop robust data analytics methods that provide viable damage assessment results when data

quality is significantly impacted by natural disasters (e.g., erroneous data or missing data).

E-8


The use case interfaces with a number of focus areas and research thrusts identified in the Roadmap,

including sensor devices, communications, and data management and analytics. It also involves the

optimal sensor placement identified in the roadmap.

E.7 USE CASE: OPTIMIZING GRID OPERATION WITH ENHANCED DATA SPANNING

TRANSMISSION DISTRIBUTION AND GENERATION

Objective:

Develop sensing, data analytics, and communication infrastructure for achieving generation, transmission,

and distribution (G, T, and D) operation with a high penetration of distributed resources

Description:

Electric power systems are becoming more complex with the deployment of DER, storage, and

responsive customer loads. The majority of electric distribution systems have traditionally been designed

and operated as radial systems providing one-way power flow, whereas transmission systems have been

designed for networks and two-way power flow. In the case of distribution systems, the introduction and

continued deployment of DER and energy storage introduces bidirectional power flow on these

distribution circuits. Responsive customer loads change the demand of customer loads in response to

utility signals or pricing. In the case of transmission systems, transmission lines provide the vital link

between generation and distribution systems. The introduction of greater renewable energy sources on

transmission systems results in utilities needing to relay more on firm power sources and responsive

industrial loads when there is insufficient solar or wind power to provide renewable energy. Furthermore,

DER can impact operating voltage profiles and reactive power requirements as well as protection

schemes. Additionally, with increased variable renewable penetration at both transmission and

distribution levels, grid stability assessments need to be performed faster in an online fashion. Offline

simulations and machine learning techniques applied to new sensor data can enable the application of

stability assessments and indices in real time. There is increased need to use model-free methods that

directly use data from sensors to learn system stability and perform timely control actions.


This proposed use case targets the following research objectives:

1. Develop sensor and data analytics needed to achieve high DER penetration on a power transmission

and distribution system. Work with a distribution system utility to understand the needs to achieve

this within a particular system.

2. Develop sensor and data analytics needed for future system operation with high DER penetration on

both the transmission and distribution levels.

3. Develop theoretical foundations and research demonstrations for autonomous energy grids that will

use heterogenous sensor data proliferated in transmission and distribution grids and will perform real-

time data-driven stability assessments and optimal control for ensuring reliability and economics.

E-9


A future challenge for the modern grid is understanding the tolerance of existing transmission and

distribution systems for accepting high levels of DER penetration. This area involves greater need not

only for sensors but also for data analytics, communications, controls, and protection. As the modern grid

evolves, new design changes in the transmission and distribution systems may alleviate some of these

needs, but there will also be legacy systems to accommodate.

E.8 USE CASE: DETECTION OF ENERGY THEFT AND UNREGISTERED DER

Objective:

Detect rogue DER, identification/development of low-cost sensor technology, and data analytics for

detection of energy losses due to energy theft on power systems.

Description:

The deployment of DER is occurring with the placement of PV and wind systems on transmission and

distribution systems. Large DER systems, e.g., 1 MW or larger, are being developed and deployed by

commercial entities and are regulated by the respective utility systems to which they provide renewable

power. However, in the case of residential-sized DER, not all of these sources are registered with the

utility system, especially when the source is installed behind the customer kWh meter. In the case of a

DER deployed at an existing home that has been without this energy source initially over a period of time,

it may be possible to detect the presence of the DER by detecting the decrease in energy demand.

However, there may be a need for better detection of these rogue distributed sources. In fact, the goal is to

be able to both detect a source and its output not only to forecast the capability of these sources but also to

determine how to control voltage regulators, capacitor banks, and reactive power sources to ensure the

correct regulation of voltage profiles and overall power factor on distribution systems/lines.

In the case of the EPB, one of 152 power distributors of the Tennessee Valley Authority, the utility not

only must maintain the distribution system voltages of its secondary within an adequate operating range

to maintain 120 V ±5%, but also must maintain its aggregated power factor level within an adequate

operating range, such as 0.95 leading to 0.95 lagging. Traditionally, EPB has been able to meet these

requirements using its load-tap-changing transformers and capacitors banks at EPB distribution

substations and line voltage regulators on their distribution circuits. However, the growing number and

capacity of DER on distribution circuits introduces more of a challenge for voltage regulation and

reactive power support. One of the key challenges is the need for adjustable settings for line regulators to

compensate for the presence of DER. The introduction of smart meters and telemetry has enabled

distribution systems to better monitor their energy loads and detect energy losses. However, unregistered

distributed sources make it difficult to determine the difference between reduced energy load and losses,

especially energy theft.


The proposed use case targets four primary research objectives:

1. Develop and demonstrate low-cost sensing of previously unknown DER on distribution circuits.

2. Demonstrate this technology (e.g., on the EPB system) and adjust line voltage regulator settings via

telemetry in response to changing operating states of DER in the distribution system.

E-10

3. Develop analytical methods to determine the true generating behavior of behind-the-meter resources

from electrical parameter measurements.

4. Develop low-cost sensing technologies and data analytics that leverage existing sensing and

measurement technologies and advanced data analytic methods for detecting energy losses due to

theft.


The detection of energy theft is not an area restricted to developing countries only. The evolution of the

smart grid with the deployment of DER makes it more difficult for distribution systems to detect energy

losses due to power transmission versus those due to energy theft by actors unlawfully tapping into

distribution systems.

Successful detection of previously unknown DER in distribution systems interfaces with a number of

focus areas and research thrusts identified in the Roadmap. They span the areas of new sensor device

development, data analytics, and communications systems, as well as control development and

applications. The need for this sensing capability also interfaces with the development of the sensor

optimization placement tool (SPOT) to provide optimal placement for detection and accommodation of

DER.

U.S. Department of Energy National Energy Technology Laboratory 626 Cochrans Mill Road P.O. Box 10940 Pittsburgh, PA 15236-0940 412-386-4984

Date post:	22-Feb-2020
Category:	Documents
Upload:	others
View:	3 times
Download:	0 times